1
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Henry B, Merz M, Hoang H, Abdulkarim G, Wosik J, Schoettker P. Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits. Blood Press 2024; 33:2304190. [PMID: 38245864 DOI: 10.1080/08037051.2024.2304190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.
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Affiliation(s)
- Benoit Henry
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maxime Merz
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Harry Hoang
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ghaith Abdulkarim
- Neuro-Informatics Laboratory, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jedrek Wosik
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Patrick Schoettker
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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2
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Xiao X, Yin J, Xu J, Tat T, Chen J. Advances in Machine Learning for Wearable Sensors. ACS NANO 2024. [PMID: 39145724 DOI: 10.1021/acsnano.4c05851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.
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Affiliation(s)
- Xiao Xiao
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Junyi Yin
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jing Xu
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Trinny Tat
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
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3
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Yao S, Ma Y, Xu K, Liu X. 3D Porous Graphene Architecture Integrated with Cu 2O for Enhanced Electrochemical Sensing Performance. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024. [PMID: 39140422 DOI: 10.1021/acs.langmuir.4c01966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Construction and functionalization of a 3D graphene architecture are crucial to harness and extend the unique features of graphene and thus essential for its numerous conventional and novel applications. Herein, a 3D honeycomb-patterned porous graphene architecture is constructed through a facile and low-cost self-assembly process and then integrated with Cu2O nanoparticles via a simple electrodeposition procedure. The 3D porous graphene structure is prepared by the breath figure method using a graphene oxide (GO)-based complex in which GO is modified by a surfactant as the casting material. Benefiting from the intercalation of the surfactant between the GO nanosheets and the fabrication of a 3D porous structure, the aggregation inhibition of GO nanosheets and increases in accessible surface area are realized at both nano- and microscales, resulting in good electrochemical performance. Moreover, the deposition of Cu2O nanoparticles can further improve the electrochemical sensing performance of the porous reduced graphene oxide (rGO) structure. Extremely low detection limit (30.72 nM) with a linear range of 0 μM to 30 μM, excellent anti-interference, repeatability, reproducibility, stability, and high accuracy for actual sample testing are shown when the 3D porous Cu2O/rGO film is applied as an electrochemical sensor for DA detection. This work provides not only a superior electrochemical biosensor but also a simple, yet effective and general strategy for the construction and functionalization of a 3D graphene structure.
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Affiliation(s)
- Shun Yao
- School of Material Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150040, China
| | - Yingyi Ma
- School of Material Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150040, China
| | - Kaizheng Xu
- School of Material Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150040, China
| | - Xiaoting Liu
- School of Material Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150040, China
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4
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Kammarchedu V, Asgharian H, Zhou K, Soltan Khamsi P, Ebrahimi A. Recent advances in graphene-based electroanalytical devices for healthcare applications. NANOSCALE 2024; 16:12857-12882. [PMID: 38888429 PMCID: PMC11238565 DOI: 10.1039/d3nr06137j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Graphene, with its outstanding mechanical, electrical, and biocompatible properties, stands out as an emerging nanomaterial for healthcare applications, especially in building electroanalytical biodevices. With the rising prevalence of chronic diseases and infectious diseases, such as the COVID-19 pandemic, the demand for point-of-care testing and remote patient monitoring has never been greater. Owing to their portability, ease of manufacturing, scalability, and rapid and sensitive response, electroanalytical devices excel in these settings for improved healthcare accessibility, especially in resource-limited settings. The development of different synthesis methods yielding large-scale graphene and its derivatives with controllable properties, compatible with device manufacturing - from lithography to various printing methods - and tunable electrical, chemical, and electrochemical properties make it an attractive candidate for electroanalytical devices. This review article sheds light on how graphene-based devices can be transformative in addressing pressing healthcare needs, ranging from the fundamental understanding of biology in in vivo and ex vivo studies to early disease detection and management using in vitro assays and wearable devices. In particular, the article provides a special focus on (i) synthesis and functionalization techniques, emphasizing their suitability for scalable integration into devices, (ii) various transduction methods to design diverse electroanalytical device architectures, (iii) a myriad of applications using devices based on graphene, its derivatives, and hybrids with other nanomaterials, and (iv) emerging technologies at the intersection of device engineering and advanced data analytics. Finally, some of the major hurdles that graphene biodevices face for translation into clinical applications are discussed.
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Affiliation(s)
- Vinay Kammarchedu
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
- Center for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Heshmat Asgharian
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Keren Zhou
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Pouya Soltan Khamsi
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Aida Ebrahimi
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
- Center for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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5
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Li J, Chu H, Chen Z, Yiu CK, Qu Q, Li Z, Yu X. Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring. ACS NANO 2024; 18:17407-17438. [PMID: 38923501 DOI: 10.1021/acsnano.4c04291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Continuous blood pressure (BP) tracking provides valuable insights into the health condition and functionality of the heart, arteries, and overall circulatory system of humans. The rapid development in flexible and wearable electronics has significantly accelerated the advancement of wearable BP monitoring technologies. However, several persistent challenges, including limited sensing capabilities and stability of flexible sensors, poor interfacial stability between sensors and skin, and low accuracy in BP estimation, have hindered the progress in wearable BP monitoring. To address these challenges, comprehensive innovations in materials design, device development, system optimization, and modeling have been pursued to improve the overall performance of wearable BP monitoring systems. In this review, we highlight the latest advancements in flexible and wearable systems toward continuous noninvasive BP tracking with a primary focus on materials development, device design, system integration, and theoretical algorithms. Existing challenges, potential solutions, and further research directions are also discussed to provide theoretical and technical guidance for the development of future wearable systems in continuous ambulatory BP measurement with enhanced sensing capability, robustness, and long-term accuracy.
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Affiliation(s)
- Jian Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Hongwei Chu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Zhenlin Chen
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Chun Ki Yiu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Qing'ao Qu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zhiyuan Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China
- Hong Kong Institute for Clean Energy, City University of Hong Kong, Kowloon, Hong Kong, China
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6
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Khan A, Rashid M, Grabher G, Hossain G. Autonomous Triboelectric Smart Textile Sensor for Vital Sign Monitoring. ACS APPLIED MATERIALS & INTERFACES 2024; 16:31807-31816. [PMID: 38847366 DOI: 10.1021/acsami.4c04689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Wearable smart textile sensors for monitoring vital signs are fast, noninvasive, and highly desirable for personalized health management to diagnose health anomalies such as cardiovascular diseases and respiratory dysfunction. Traditional biosignal sensors, with power consumption issues, constrain the use of wearable medical devices. This study introduces an autonomous triboelectric smart textile sensor (AUTS) made of reduced graphene oxide/manganese dioxide/polydimethylsiloxane (RGO-M-PDMS) and polytetrafluoroethylene (TEFLON)-knitted silver electrode, offering promise for vital sign monitoring with self-powering, flexibility, and wearability. The sensor exhibits impressive output performance, with a sensitivity of 7.8 nA/kPa, response time of ≈40 ms, good stability of >15,000 cycles, stretchability of up to 40%, and machine washability of >20 washes. The AUTS has been integrated to the TriBreath respiratory belt for monitoring respiratory signals and pulse strap for pulse signals concurrently at different body pulse points. These sensors wirelessly transmitted the acquired biosignals to a smartphone, demonstrating the potential of a self-powered and real-time vital sign monitoring system.
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Affiliation(s)
- Ashaduzzaman Khan
- V-Trion Textile Research GmbH, Millennium Park-15, 6890 Lustenau, Austria
| | - Mamunur Rashid
- V-Trion Textile Research GmbH, Millennium Park-15, 6890 Lustenau, Austria
| | - Günter Grabher
- Grabher Group GmbH, Augarten Strasse 27, 6890 Lustenau, Austria
| | - Gaffar Hossain
- V-Trion Textile Research GmbH, Millennium Park-15, 6890 Lustenau, Austria
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7
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Sun Y, Cui J, Feng S, Cui J, Guo Y, Liang C, Gao W, Lu Z, Liu F, Zhang B. Projection Stereolithography 3D Printing High-Conductive Hydrogel for Flexible Passive Wireless Sensing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2400103. [PMID: 38573809 DOI: 10.1002/adma.202400103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Hydrogel-based electronics have inherent similarities to biological tissues and hold potential for wearable applications. However, low conductivity, poor stretchability, nonpersonalizability, and uncontrollable dehydration during use limit their further development. In this study, projection stereolithography 3D printing high-conductive hydrogel for flexible passive wireless sensing is reported. The prepared photocurable silver-based hydrogel is rapidly planarized into antenna shapes on substrates using surface projection stereolithography. After partial dehydration, silver flakes within the circuits form sufficient conductive pathways to achieve high conductivity (387 S cm-1). By sealing the circuits to prevent further dehydration, the resistance remains stable when tensile strain is less than 100% for at least 30 days. Besides, the sealing materials provide versatile functionalities, such as stretchability and shape memory property. Customized flexible radio frequency identification tags are fabricated by integrating with commercial chips to complete the accurate recognition of eye movement, realizing passive wireless sensing.
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Affiliation(s)
- Yongding Sun
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Jin Cui
- School of Information and Science Technology, Northwest University, 1 Xuefu Street, Xi'an, 710127, China
| | - Shiwei Feng
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Jingjing Cui
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Yunlong Guo
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Chen Liang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Weizi Gao
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Zhe Lu
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Fukang Liu
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Biao Zhang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
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8
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Liu S, Akinwande D, Kireev D, Incorvia JAC. Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems. NANO LETTERS 2024. [PMID: 38819288 DOI: 10.1021/acs.nanolett.4c00739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.
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Affiliation(s)
- Samuel Liu
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Deji Akinwande
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Dmitry Kireev
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
- Department of Biomedical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Jean Anne C Incorvia
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
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9
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Li C, Wang T, Zhou S, Sun Y, Xu Z, Xu S, Shu S, Zhao Y, Jiang B, Xie S, Sun Z, Xu X, Li W, Chen B, Tang W. Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment. RESEARCH (WASHINGTON, D.C.) 2024; 7:0366. [PMID: 38783913 PMCID: PMC11112600 DOI: 10.34133/research.0366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/02/2024] [Indexed: 05/25/2024]
Abstract
Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.
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Affiliation(s)
- Chengyu Li
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingyu Wang
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Zhou
- Department of Orthopaedics,
Peking University Third Hospital, Beijing 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine,
Ministry of Education, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Yanshuo Sun
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zijie Xu
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuxing Xu
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sheng Shu
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yi Zhao
- Department of Orthopaedics,
Peking University Third Hospital, Beijing 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine,
Ministry of Education, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Bing Jiang
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- Center on Nanoenergy Research, School of Physical Science and Technology,
Guangxi University, Nanning 530004, China
| | - Shiwang Xie
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuoran Sun
- Department of Orthopaedics,
Peking University Third Hospital, Beijing 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine,
Ministry of Education, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Xiaowei Xu
- Guangdong Provincial People’s Hospital,
Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weishi Li
- Department of Orthopaedics,
Peking University Third Hospital, Beijing 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine,
Ministry of Education, Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, China
| | - Baodong Chen
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Tang
- Beijing Institute of Nanoenergy and Nanosystems,
Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology,
University of Chinese Academy of Sciences, Beijing 100049, China
- Center on Nanoenergy Research, School of Physical Science and Technology,
Guangxi University, Nanning 530004, China
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10
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Lee H, Johnson Z, Denton S, Liu N, Akinwande D, Porter E, Kireev D. A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography. Physiol Meas 2024; 45:055003. [PMID: 38599226 DOI: 10.1088/1361-6579/ad3d26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/10/2024] [Indexed: 04/12/2024]
Abstract
Objective.Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising.Approach.In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs).Main results.We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape.Significance.In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.
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Affiliation(s)
- Hannah Lee
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
| | - Zane Johnson
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, United States of America
| | - Spencer Denton
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
| | - Ning Liu
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
| | - Deji Akinwande
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, United States of America
| | - Emily Porter
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Dmitry Kireev
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, United States of America
- Department of Biomedical Engineering, The University of Massachusetts Amherst, Amherst, MA, United States of America
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11
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Xie G, Guo S, Li B, Hou W, Zhang Y, Pan J, Wei X, Sun SK. Nonmetallic graphite for tumor magnetic hyperthermia therapy. Biomaterials 2024; 306:122498. [PMID: 38310828 DOI: 10.1016/j.biomaterials.2024.122498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/17/2024] [Accepted: 01/31/2024] [Indexed: 02/06/2024]
Abstract
Magnetic hyperthermia therapy (MHT) has garnered immense interest due to its exceptional spatiotemporal specificity, minimal invasiveness and remarkable tissue penetration depth. Nevertheless, the limited magnetothermal heating capability and the potential toxicity of metal ions in magnetic materials based on metallic elements significantly impede the advancement of MHT. Herein, we introduce the concept of nonmetallic materials, with graphite (Gra) as a proof of concept, as a highly efficient and biocompatible option for MHT of tumors in vivo for the first time. The Gra exhibits outstanding magnetothermal heating efficacy owing to the robust eddy thermal effect driven by its excellent electrical conductivity. Furthermore, being composed of carbon, Gra offers superior biocompatibility as carbon is an essential element for all living organisms. Additionally, the Gra boasts customizable shapes and sizes, low cost, and large-scale production capability, facilitating reproducible and straightforward manufacturing of various Gra implants. In a mouse tumor model, Gra-based MHT successfully eliminates the tumors at an extremely low magnetic field intensity, which is less than one-third of the established biosafety threshold. This study paves the way for the development of high-performance magnetocaloric materials by utilizing nonmetallic materials in place of metallic ones burdened with inherent limitations.
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Affiliation(s)
- Guangchao Xie
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Shuyue Guo
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Bingjie Li
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenjing Hou
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Yanqi Zhang
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Jinbin Pan
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
| | - Shao-Kai Sun
- School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, China.
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12
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Ramezani M, Kim JH, Liu X, Ren C, Alothman A, De-Eknamkul C, Wilson MN, Cubukcu E, Gilja V, Komiyama T, Kuzum D. High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings. NATURE NANOTECHNOLOGY 2024; 19:504-513. [PMID: 38212523 DOI: 10.1038/s41565-023-01576-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 11/16/2023] [Indexed: 01/13/2024]
Abstract
Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.
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Affiliation(s)
- Mehrdad Ramezani
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jeong-Hoon Kim
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Xin Liu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Chi Ren
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Abdullah Alothman
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Chawina De-Eknamkul
- Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA
| | - Madison N Wilson
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ertugrul Cubukcu
- Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Takaki Komiyama
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
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13
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Li H, Tan P, Rao Y, Bhattacharya S, Wang Z, Kim S, Gangopadhyay S, Shi H, Jankovic M, Huh H, Li Z, Maharjan P, Wells J, Jeong H, Jia Y, Lu N. E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin. Chem Rev 2024; 124:3220-3283. [PMID: 38465831 DOI: 10.1021/acs.chemrev.3c00626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.
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Affiliation(s)
- Hongbian Li
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Philip Tan
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yifan Rao
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sarnab Bhattacharya
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zheliang Wang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sangjun Kim
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Susmita Gangopadhyay
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Hongyang Shi
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Matija Jankovic
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Heeyong Huh
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhengjie Li
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Pukar Maharjan
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jonathan Wells
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Hyoyoung Jeong
- Department of Electrical and Computer Engineering, University of California Davis, Davis, California 95616, United States
| | - Yaoyao Jia
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
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14
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Wang H, Ding Q, Luo Y, Wu Z, Yu J, Chen H, Zhou Y, Zhang H, Tao K, Chen X, Fu J, Wu J. High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309868. [PMID: 38095146 DOI: 10.1002/adma.202309868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/03/2023] [Indexed: 12/22/2023]
Abstract
Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Qiongling Ding
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Yibing Luo
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Zixuan Wu
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Jiahao Yu
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Huizhi Chen
- Guangdong Provincial Key Laboratory of Research and Development of Natural Drugs and School of Pharmacy, Guangdong Medical University, Dongguan, 523808, P. R. China
- The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523808, P. R. China
| | - Yubin Zhou
- Guangdong Provincial Key Laboratory of Research and Development of Natural Drugs and School of Pharmacy, Guangdong Medical University, Dongguan, 523808, P. R. China
- The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523808, P. R. China
| | - He Zhang
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, National Engineering Research Center of Novel Equipment for Polymer Processing, Key Laboratory of Polymer Processing Engineering (SCUT) Ministry of Education, South China University of Technology, Guangzhou, 510641, P. R. China
| | - Kai Tao
- Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiaoliang Chen
- Micro- and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Jun Fu
- School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, 510275, China
| | - Jin Wu
- State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510275, China
- Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, National Engineering Research Center of Novel Equipment for Polymer Processing, Key Laboratory of Polymer Processing Engineering (SCUT) Ministry of Education, South China University of Technology, Guangzhou, 510641, P. R. China
- State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, 610065, People's Republic of China
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15
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Shin JH, Choi JY, June K, Choi H, Kim TI. Polymeric Conductive Adhesive-Based Ultrathin Epidermal Electrodes for Long-Term Monitoring of Electrophysiological Signals. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2313157. [PMID: 38421078 DOI: 10.1002/adma.202313157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/08/2024] [Indexed: 03/02/2024]
Abstract
Electrophysiology, exploring vital electrical phenomena in living organisms, anticipates broader integration into daily life through wearable devices and epidermal electrodes. However, addressing the challenges of the electrode durability and motion artifacts is essential to enable continuous and long-term biopotential signal monitoring, presenting a hurdle for its seamless implementation in daily life. To address these challenges, an ultrathin polymeric conductive adhesive, poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate)/polyvinyl alcohol/d-sorbitol (PPd) electrode with enhanced adhesion, stretchability, and skin conformability, is presented. The skin conformability and stability of electrodes is designed by theoretical criteria obtained by mechanical analysis. Thus, impedance stability is obtained over 1-week of daily life, and the PPd electrode addresses the challenges related to durability during prolonged usage. Proving stability in electromyography (EMG) signals during high-intensity exercise, the wireless PPd measurement system exhibits high signal-to-noise ratio (SNR) signals even in situations involving significant and repetitive skin deformation. Throughout continuous 1 week-long electrocardiogram (ECG) monitoring in daily life, the system consistently preserves signal quality, underscoring the heightened durability and applicability of the PPd measurement system.
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Affiliation(s)
- Joo Hwan Shin
- School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
| | - Ji Yeong Choi
- School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
| | - Keonuk June
- School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
| | - Hyesu Choi
- School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
| | - Tae-Il Kim
- School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea
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16
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Hua Q, Shen G. Low-dimensional nanostructures for monolithic 3D-integrated flexible and stretchable electronics. Chem Soc Rev 2024; 53:1316-1353. [PMID: 38196334 DOI: 10.1039/d3cs00918a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Flexible/stretchable electronics, which are characterized by their ultrathin design, lightweight structure, and excellent mechanical robustness and conformability, have garnered significant attention due to their unprecedented potential in healthcare, advanced robotics, and human-machine interface technologies. An increasing number of low-dimensional nanostructures with exceptional mechanical, electronic, and/or optical properties are being developed for flexible/stretchable electronics to fulfill the functional and application requirements of information sensing, processing, and interactive loops. Compared to the traditional single-layer format, which has a restricted design space, a monolithic three-dimensional (M3D) integrated device architecture offers greater flexibility and stretchability for electronic devices, achieving a high-level of integration to accommodate the state-of-the-art design targets, such as skin-comfort, miniaturization, and multi-functionality. Low-dimensional nanostructures possess small size, unique characteristics, flexible/elastic adaptability, and effective vertical stacking capability, boosting the advancement of M3D-integrated flexible/stretchable systems. In this review, we provide a summary of the typical low-dimensional nanostructures found in semiconductor, interconnect, and substrate materials, and discuss the design rules of flexible/stretchable devices for intelligent sensing and data processing. Furthermore, artificial sensory systems in 3D integration have been reviewed, highlighting the advancements in flexible/stretchable electronics that are deployed with high-density, energy-efficiency, and multi-functionalities. Finally, we discuss the technical challenges and advanced methodologies involved in the design and optimization of low-dimensional nanostructures, to achieve monolithic 3D-integrated flexible/stretchable multi-sensory systems.
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Affiliation(s)
- Qilin Hua
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China.
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
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17
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Namkoong M, McMurray J, Branan K, Hernandez J, Gandhi M, Ida-Oze S, Cote G, Tian L. Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring. ADVANCED MATERIALS TECHNOLOGIES 2024; 9:2301407. [PMID: 38665229 PMCID: PMC11044990 DOI: 10.1002/admt.202301407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Indexed: 04/28/2024]
Abstract
Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.
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Affiliation(s)
- Myeong Namkoong
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Justin McMurray
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Kimberly Branan
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Joanna Hernandez
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Mishika Gandhi
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Samuel Ida-Oze
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Gerard Cote
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Limei Tian
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
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18
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Kim H, Zhao HL, van der Zande AM. Stretchable Thin-Film Transistors Based on Wrinkled Graphene and MoS 2 Heterostructures. NANO LETTERS 2024; 24:1454-1461. [PMID: 38214495 DOI: 10.1021/acs.nanolett.3c05091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Two-dimensional (2D) materials are outstanding candidates for stretchable electronics, but a significant challenge is their heterogeneous integration into stretchable geometries on soft substrates. Here, we demonstrate a strategy for stretchable thin film transistors (2D S-TFT) based on wrinkled heterostructures on elastomer substrates where 2D materials formed the gate, source, drain, and channel and characterized them with Raman spectroscopy and transport measurements. The 2D S-TFTs had initial mobility of 4.9 ± 0.7 cm2/(V s). The wrinkling reduced the strain transferred into the 2D materials by a factor of 50, allowing a substrate stretch of up to 23% that could be cycled thousands of times without electrical degradation. The stretch did not alter the mobility but did lead to strain-induced threshold voltage shifts by ΔVT = -1.9 V. These 2D S-TFTs form the foundation for stretchable integrated circuits and enable investigations of the impact of heterogeneous strain on electron transport.
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Affiliation(s)
- Hyunchul Kim
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - He Lin Zhao
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Arend M van der Zande
- Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Materials Research Lab, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Holonyak Micro and Nano Technology Lab, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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19
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Gong S, Lu Y, Yin J, Levin A, Cheng W. Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare. Chem Rev 2024; 124:455-553. [PMID: 38174868 DOI: 10.1021/acs.chemrev.3c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.
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Affiliation(s)
- Shu Gong
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Yan Lu
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Jialiang Yin
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Arie Levin
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Wenlong Cheng
- Department of Chemical & Biological Engineering, Monash University, Clayton, Victoria 3800, Australia
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20
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Katiyar AK, Hoang AT, Xu D, Hong J, Kim BJ, Ji S, Ahn JH. 2D Materials in Flexible Electronics: Recent Advances and Future Prospectives. Chem Rev 2024; 124:318-419. [PMID: 38055207 DOI: 10.1021/acs.chemrev.3c00302] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Flexible electronics have recently gained considerable attention due to their potential to provide new and innovative solutions to a wide range of challenges in various electronic fields. These electronics require specific material properties and performance because they need to be integrated into a variety of surfaces or folded and rolled for newly formatted electronics. Two-dimensional (2D) materials have emerged as promising candidates for flexible electronics due to their unique mechanical, electrical, and optical properties, as well as their compatibility with other materials, enabling the creation of various flexible electronic devices. This article provides a comprehensive review of the progress made in developing flexible electronic devices using 2D materials. In addition, it highlights the key aspects of materials, scalable material production, and device fabrication processes for flexible applications, along with important examples of demonstrations that achieved breakthroughs in various flexible and wearable electronic applications. Finally, we discuss the opportunities, current challenges, potential solutions, and future investigative directions about this field.
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Affiliation(s)
- Ajit Kumar Katiyar
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Anh Tuan Hoang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Duo Xu
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Juyeong Hong
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Beom Jin Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Seunghyeon Ji
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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21
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Zhang Y, Hou W, Chang R, Yao X, Xu Y. Ultrafast alternating-current exfoliation toward large-scale synthesis of graphene and its application for flexible supercapacitors. J Colloid Interface Sci 2024; 654:246-257. [PMID: 37839241 DOI: 10.1016/j.jcis.2023.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/30/2023] [Accepted: 10/08/2023] [Indexed: 10/17/2023]
Abstract
To facilitate the transition of laboratory research to industrial applications, it is critical to establish a reliable protocol for the mass synthesis of high-quality graphene. Here, we present an efficient electrochemical intercalation-based exfoliation approach utilizing alternating current that allows for the production of sub-kilogram quantities of graphene. This strategy involves repeatedly intercalating foreign anions and cations into the interlayer gaps of dual-graphite electrodes, accelerating the graphite expansion process and maximizing the exfoliation efficiency of both electrodes while inhibiting excessive anodic oxidation. The exfoliation process leads to high-yield graphene nanosheets (92 %, primarily 1-3 layers) with minimal structural deterioration (ID/IG ratio of 0.05), high purity (2.1 at% oxygen), and outstanding electrical property (7.28 × 104 S m-1). Notably, our scaled-up manufacturing technique produces a record-breaking throughput of 135 g h-1, improving on the best-reported exfoliation efficiency with direct current by 35%. Furthermore, the as-made graphene demonstrates a large reversible capacity of 102 mF cm-2 for flexible supercapacitors, with robust cyclability with 99.5% after 10,000 cycles, excellent mechanical flexibility, and exceptional serial integration for adjustable voltage output. The efficient and scalable method presents a significant advancement in the large-scale manufacture of graphene, with potential for widespread industrial applications.
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Affiliation(s)
- Yuan Zhang
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Engineering Research Center of Advanced Energy Materials & Devices, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Wenqiang Hou
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Engineering Research Center of Advanced Energy Materials & Devices, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Rui Chang
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Engineering Research Center of Advanced Energy Materials & Devices, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xianghua Yao
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Engineering Research Center of Advanced Energy Materials & Devices, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Youlong Xu
- Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education & International Center for Dielectric Research, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Shaanxi Engineering Research Center of Advanced Energy Materials & Devices, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an 710049, China.
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22
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Wang M, Zheng X, Ye X, Liu W, Zhang B, Zhang Z, Zhai R, Ning Y, Li H, Song A. High-Performance Photodetectors Based on Semiconducting Graphene Nanoribbons. NANO LETTERS 2024; 24:165-171. [PMID: 38010996 PMCID: PMC10786164 DOI: 10.1021/acs.nanolett.3c03563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023]
Abstract
The inherent zero-band gap nature of graphene and its fast photocarrier recombination rate result in poor optical gain and responsivity when graphene is used as the light absorption medium in photodetectors. Here, semiconducting graphene nanoribbons with a direct bandgap of 1.8 eV are synthesized and employed to construct a vertical heterojunction photodetector. At a bias voltage of -5 V, the photodetector exhibits a responsivity of 1052 A/W, outperforming previous graphene-based heterojunction photodetectors by several orders of magnitude. The achieved detectivity of 3.13 × 1013 Jones and response time of 310 μs are also among the best values for graphene-based heterojunction photodetectors reported until date. Furthermore, even under zero bias, the photodetector demonstrates a high responsivity and detectivity of 1.04 A/W and 2.45 × 1012 Jones, respectively. The work shows a great potential of graphene nanoribbon-based photodetection technology.
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Affiliation(s)
- Mingyang Wang
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Xiaoxiao Zheng
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Xiaoling Ye
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Wencheng Liu
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Baoqing Zhang
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Zihao Zhang
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Rongli Zhai
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
| | - Yafei Ning
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
- Shenzhen
Research Institute of Shandong University, Shenzhen 518063, China
| | - Hu Li
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
- Shenzhen
Research Institute of Shandong University, Shenzhen 518063, China
| | - Aimin Song
- Shandong
Technology Centre of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan 250101, China
- Department
of Electrical and Electronic Engineering, University of Manchester, M13 9PL Manchester, U.K.
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23
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Tang C, Liu Z, Hu Q, Jiang Z, Zheng M, Xiong C, Wang S, Yao S, Zhao Y, Wan X, Liu G, Sun Q, Wang ZL, Li L. Unconstrained Piezoelectric Vascular Electronics for Wireless Monitoring of Hemodynamics and Cardiovascular Health. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2304752. [PMID: 37691019 DOI: 10.1002/smll.202304752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/15/2023] [Indexed: 09/12/2023]
Abstract
The patient-centered healthcare requires timely disease diagnosis and prognostic assessment, calling for individualized physiological monitoring. To assess the postoperative hemodynamic status of patients, implantable blood flow monitoring devices are highly expected to deliver real time, long-term, sensitive, and reliable hemodynamic signals, which can accurately reflect multiple physiological conditions. Herein, an implantable and unconstrained vascular electronic system based on a piezoelectric sensor immobilized is presented by a "growable" sheath around continuously growing arterial vessels for real-timely and wirelessly monitoring of hemodynamics. The piezoelectric sensor made of circumferentially aligned polyvinylidene fluoride nanofibers around pulsating artery can sensitively perceive mechanical signals, and the growable sheath bioinspired by the structure and function of leaf sheath has elasticity and conformal shape adaptive to the dynamically growing arterial vessels to avoid growth constriction. With this integrated and smart design, long-term, wireless, and sensitive monitoring of hemodynamics are achieved and demonstrated in rats and rabbits. It provides a simple and versatile strategy for designing implantable sensors in a less invasive way.
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Affiliation(s)
- Chuyu Tang
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Zhirong Liu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Quanhong Hu
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Zhuoheng Jiang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Mingjia Zheng
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Cheng Xiong
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Shaobo Wang
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Shuncheng Yao
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yunchao Zhao
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Xingyi Wan
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Guanlin Liu
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
| | - Qijun Sun
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
- Georgia Institute of Technology, Atlanta, GA 30332-0245, USA
| | - Linlin Li
- Center on Nanoenergy Research, School of Physical Science & Technology, Guangxi University, Nanning, 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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24
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Hou J, Nesaragi N, Tronstad C. Electrical bioimpedance in the era of artificial intelligence. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:1-3. [PMID: 38304720 PMCID: PMC10830329 DOI: 10.2478/joeb-2024-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Indexed: 02/03/2024]
Affiliation(s)
- Jie Hou
- Department of Physics, University of Oslo, 0316Oslo, Norway
| | | | - Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, 0372Oslo, Norway
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25
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Liu Z, Zhang Y, Zhou C. BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG. Comput Biol Med 2024; 168:107795. [PMID: 38056206 DOI: 10.1016/j.compbiomed.2023.107795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Physiological parameter monitoring based on photoplethysmography (PPG) detection has the advantage of fast, portable, and non-invasive. Changes in the morphology of the PPG waveform can reflect the effect of arterial elasticity changes on blood pressure (BP). However, machine learning models and non-recurrent neural network models typically ignore the time-dependency of continuous PPG data, leading to the decrease of accuracy or the increased calibration frequency. OBJECTIVE This paper proposes a BiGRU model with attention to estimate BP trends, which uses a single-channel PPG signal combined with demographic information to estimate continuous BP trends point-by-point and to discuss the impact of calibration cycle. METHODS This paper selects 15 typical subjects from two groups with/without cardiovascular disease (CVD) and evaluates the model performance. Regarding the calibration frequency problem, we set two modes of non-calibration and calibration to validate the results of blood pressure trends estimation. RESULTS In the calibration mode, the estimation errors (ME ± STD) of SBP for CVD/non-CVD groups are 0.91 ± 10.58 mmHg/0.17 ± 10.06 mmHg respectively, and DBP are 0.34 ± 5.28 mmHg/-0.19 ± 5.36 mmHg; in the non-calibration mode, the estimation errors of SBP for CVD/non-CVD groups are 0.27 ± 9.87 mmHg/-0.82 ± 9.92 mmHg respectively, and DBP are -0.63 ± 3.28 mmHg/0.80 ± 4.93 mmHg. CONCLUSIONS The results show that the proposed model has high accuracy in estimating BP levels, which is expected to achieve real-time, long-term continuous BP trends monitoring in wearable devices.
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Affiliation(s)
- Ziyi Liu
- Guangdong Transtek Medical Electronics Co., Ltd., Zhongshan, 52843, People's Republic of China
| | - Yiming Zhang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Congcong Zhou
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China; Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, National Engineering Research Center for Innovation and Application of Minimally Invasive Devices, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China.
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26
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Nguyen DV, Mills D, Tran CD, Nguyen T, Nguyen H, Tran TL, Song P, Phan HP, Nguyen NT, Dao DV, Bell J, Dinh T. Facile Fabrication of "Tacky", Stretchable, and Aligned Carbon Nanotube Sheet-Based Electronics for On-Skin Health Monitoring. ACS APPLIED MATERIALS & INTERFACES 2023; 15:58746-58760. [PMID: 38051258 DOI: 10.1021/acsami.3c13541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Point-of-care monitoring of physiological signals such as electrocardiogram, electromyogram, and electroencephalogram is essential for prompt disease diagnosis and quick treatment, which can be realized through advanced skin-worn electronics. However, it is still challenging to design an intimate and nonrestrictive skin-contact device for physiological measurements with high fidelity and artifact tolerance. This research presents a facile method using a "tacky" surface to produce a tight interface between the ACNT skin-like electronic and the skin. The method provides the skin-worn electronic with a stretchability of up to 70% strain, greater than that of most common epidermal electrodes. Low-density ACNT bundles facilitate the infiltration of adhesive and improve the conformal contact between the ACNT sheet and the skin, while dense ACNT bundles lessen this effect. The stretchability and conformal contact allow the ACNT sheet-based electronics to create a tight interface with the skin, which enables the high-fidelity measurement of physiological signals (the Pearson's coefficient of 0.98) and tolerance for motion artifacts. In addition, our method allows the use of degradable substrates to enable reusability and degradability of the electronics based on ACNT sheets, integrating "green" properties into on-skin electronics.
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Affiliation(s)
- Duy Van Nguyen
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Dean Mills
- School of Health and Medical Sciences, University of Southern Queensland, Brisbane 4305, Queensland, Australia
| | - Canh-Dung Tran
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Thanh Nguyen
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Hung Nguyen
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Thi Lap Tran
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Pingan Song
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Hoang-Phuong Phan
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney 1466, New South Wales, Australia
| | - Nam-Trung Nguyen
- Queensland Micro- and Nanotechnology Centre, Griffith University, Brisbane 4111, Queensland, Australia
| | - Dzung Viet Dao
- Queensland Micro- and Nanotechnology Centre, Griffith University, Brisbane 4111, Queensland, Australia
- Griffith School of Engineering, Griffith University, Gold Coast 4125, Queensland, Australia
| | - John Bell
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
| | - Toan Dinh
- School of Engineering, University of Southern Queensland, Brisbane 4300, Queensland, Australia
- Centre for Future Materials, University of Southern Queensland, Brisbane 4300, Queensland, Australia
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27
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Yao C, Sun T, Huang S, He M, Liang B, Shen Z, Huang X, Liu Z, Wang H, Liu F, Chen HJ, Xie X. Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring. ACS NANO 2023; 17:24242-24258. [PMID: 37983291 DOI: 10.1021/acsnano.3c09766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since the correlations of pulse waveforms to BP are highly individualized due to the diversity of the patients' physiological characteristics, wearable sensors based on universal designs and algorithms often fail to derive BP accurately when applied on individual patients. Herein, a wearable triboelectric pulse sensor based on a biomimetic nanopillar layer was developed and coupled with Personalized Machine Learning (ML) to provide accurate and continuous monitoring of BP. Flexible conductive nanopillars as the triboelectric layer were fabricated through soft lithography replication of a cicada wing, which could effectively enhance the sensor's output performance to detect weak signal characteristics of pulse waveform for BP derivation. The sensors were coupled with a personalized Partial Least-Squares Regression (PLSR) ML to derive unknown BP based on individual pulse characteristics with reasonable accuracy, avoiding the issue of individual variability that was encountered by General PLSR ML or formula algorithms. The cuffless and intelligent design endow this ML-sensor as a highly promising platform for the care and treatments of hypertensive patients.
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Affiliation(s)
- Chuanjie Yao
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Tiancheng Sun
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Shuang Huang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Mengyi He
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Baoming Liang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhiran Shen
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Xinshuo Huang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjie Liu
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - HaoLin Wang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Fanmao Liu
- The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou 510080, China
| | - Hui-Jiuan Chen
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Xi Xie
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
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28
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Hoang AT, Hu L, Kim BJ, Van TTN, Park KD, Jeong Y, Lee K, Ji S, Hong J, Katiyar AK, Shong B, Kim K, Im S, Chung WJ, Ahn JH. Low-temperature growth of MoS 2 on polymer and thin glass substrates for flexible electronics. NATURE NANOTECHNOLOGY 2023; 18:1439-1447. [PMID: 37500777 DOI: 10.1038/s41565-023-01460-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 06/14/2023] [Indexed: 07/29/2023]
Abstract
Recent advances in two-dimensional semiconductors, particularly molybdenum disulfide (MoS2), have enabled the fabrication of flexible electronic devices with outstanding mechanical flexibility. Previous approaches typically involved the synthesis of MoS2 on a rigid substrate at a high temperature followed by the transfer to a flexible substrate onto which the device is fabricated. A recurring drawback with this methodology is the fact that flexible substrates have a lower melting temperature than the MoS2 growth process, and that the transfer process degrades the electronic properties of MoS2. Here we report a strategy for directly synthesizing high-quality and high-crystallinity MoS2 monolayers on polymers and ultrathin glass substrates (thickness ~30 µm) at ~150 °C using metal-organic chemical vapour deposition. By avoiding the transfer process, the MoS2 quality is preserved. On flexible field-effect transistors, we achieve a mobility of 9.1 cm2 V-1 s-1 and a positive threshold voltage of +5 V, which is essential for reducing device power consumption. Moreover, under bending conditions, our logic circuits exhibit stable operation while phototransistors can detect light over a wide range of wavelengths from 405 nm to 904 nm.
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Affiliation(s)
- Anh Tuan Hoang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Luhing Hu
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Beom Jin Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Tran Thi Ngoc Van
- Department of Chemical Engineering, Hongik University, Seoul, Republic of Korea
| | - Kyeong Dae Park
- Institute for Rare Metals and Division of Advanced Materials Engineering, Kongju National University, Cheonan, Republic of Korea
| | - Yeonsu Jeong
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, Seoul, Republic of Korea
| | - Kihyun Lee
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, Seoul, Republic of Korea
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Korea
| | - Seunghyeon Ji
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Juyeong Hong
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Ajit Kumar Katiyar
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Bonggeun Shong
- Department of Chemical Engineering, Hongik University, Seoul, Republic of Korea
| | - Kwanpyo Kim
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, Seoul, Republic of Korea
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Korea
| | - Seongil Im
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, Seoul, Republic of Korea
| | - Woon Jin Chung
- Institute for Rare Metals and Division of Advanced Materials Engineering, Kongju National University, Cheonan, Republic of Korea
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
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29
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Xu C, Solomon SA, Gao W. Artificial Intelligence-Powered Electronic Skin. NAT MACH INTELL 2023; 5:1344-1355. [PMID: 38370145 PMCID: PMC10868719 DOI: 10.1038/s42256-023-00760-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 10/18/2023] [Indexed: 02/20/2024]
Abstract
Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.
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Affiliation(s)
- Changhao Xu
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Samuel A. Solomon
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
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30
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Mohammed H, Chen HB, Li Y, Sabor N, Wang JG, Wang G. Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1257-1281. [PMID: 38015673 DOI: 10.1109/tbcas.2023.3334960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.
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Kaushal JB, Raut P, Kumar S. Organic Electronics in Biosensing: A Promising Frontier for Medical and Environmental Applications. BIOSENSORS 2023; 13:976. [PMID: 37998151 PMCID: PMC10669243 DOI: 10.3390/bios13110976] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023]
Abstract
The promising field of organic electronics has ushered in a new era of biosensing technology, thus offering a promising frontier for applications in both medical diagnostics and environmental monitoring. This review paper provides a comprehensive overview of organic electronics' remarkable progress and potential in biosensing applications. It explores the multifaceted aspects of organic materials and devices, thereby highlighting their unique advantages, such as flexibility, biocompatibility, and low-cost fabrication. The paper delves into the diverse range of biosensors enabled by organic electronics, including electrochemical, optical, piezoelectric, and thermal sensors, thus showcasing their versatility in detecting biomolecules, pathogens, and environmental pollutants. Furthermore, integrating organic biosensors into wearable devices and the Internet of Things (IoT) ecosystem is discussed, wherein they offer real-time, remote, and personalized monitoring solutions. The review also addresses the current challenges and future prospects of organic biosensing, thus emphasizing the potential for breakthroughs in personalized medicine, environmental sustainability, and the advancement of human health and well-being.
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Affiliation(s)
- Jyoti Bala Kaushal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.B.K.); (P.R.)
| | - Pratima Raut
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (J.B.K.); (P.R.)
| | - Sanjay Kumar
- Durham School of Architectural Engineering and Construction, Scott Campus, University of Nebraska-Lincoln, Omaha, NE 68182, USA
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32
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Wang L, Tian S, Zhu R. A new method of continuous blood pressure monitoring using multichannel sensing signals on the wrist. MICROSYSTEMS & NANOENGINEERING 2023; 9:117. [PMID: 37744263 PMCID: PMC10511443 DOI: 10.1038/s41378-023-00590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 09/26/2023]
Abstract
Hypertension is a worldwide health problem and a primary risk factor for cardiovascular disease. Continuous monitoring of blood pressure has important clinical value for the early diagnosis and prevention of cardiovascular disease. However, existing technologies for wearable continuous blood pressure monitoring are usually inaccurate, rely on subject-specific calibration and have poor generalization across individuals, which limit their practical applications. Here, we report a new blood pressure measurement method and develop an associated wearable device to implement continuous blood pressure monitoring for new subjects. The wearable device detects cardiac output and pulse waveform features through dual photoplethysmography (PPG) sensors worn on the palmar and dorsal sides of the wrist, incorporating custom-made interface sensors to detect the wearing contact pressure and skin temperature. The detected multichannel signals are fused using a machine-learning algorithm to estimate continuous blood pressure in real time. This dual PPG sensing method effectively eliminates the personal differences in PPG signals caused by different people and different wearing conditions. The proposed wearable device enables continuous blood pressure monitoring with good generalizability across individuals and demonstrates promising potential in personal health care applications.
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Affiliation(s)
- Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Shuo Tian
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
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33
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Vyshnevyy AA, Ermolaev GA, Grudinin DV, Voronin KV, Kharichkin I, Mazitov A, Kruglov IA, Yakubovsky DI, Mishra P, Kirtaev RV, Arsenin AV, Novoselov KS, Martin-Moreno L, Volkov VS. van der Waals Materials for Overcoming Fundamental Limitations in Photonic Integrated Circuitry. NANO LETTERS 2023; 23:8057-8064. [PMID: 37615652 DOI: 10.1021/acs.nanolett.3c02051] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
With the advance of on-chip nanophotonics, there is a high demand for high-refractive-index and low-loss materials. Currently, this technology is dominated by silicon, but van der Waals (vdW) materials with a high refractive index can offer a very advanced alternative. Still, up to now, it was not clear if the optical anisotropy perpendicular to the layers might be a hindering factor for the development of vdW nanophotonics. Here, we studied WS2-based waveguides in terms of their optical properties and, particularly, in terms of possible crosstalk distance. Surprisingly, we discovered that the low refractive index in the direction perpendicular to the atomic layers improves the characteristics of such devices, mainly due to expanding the range of parameters at which single-mode propagation can be achieved. Thus, using anisotropic materials offers new opportunities and novel control knobs when designing nanophotonic devices.
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Affiliation(s)
- Andrey A Vyshnevyy
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
- Moscow Center for Advanced Studies, Kulakova str. 20, Moscow 123592, Russia
| | - Georgy A Ermolaev
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
| | - Dmitriy V Grudinin
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
| | - Kirill V Voronin
- Donostia International Physics Center (DIPC), Donostia/San Sebastián 20018, Spain
| | - Ivan Kharichkin
- Moscow Center for Advanced Studies, Kulakova str. 20, Moscow 123592, Russia
| | - Arslan Mazitov
- Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Ivan A Kruglov
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
- Moscow Center for Advanced Studies, Kulakova str. 20, Moscow 123592, Russia
| | | | - Prabhash Mishra
- Quantum Materials and Devices Laboratory, Faculty of Engineering and Technology, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi, 110025, India
| | - Roman V Kirtaev
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
| | - Aleksey V Arsenin
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
- Laboratory of Advanced Functional Materials, Yerevan State University, Yerevan 0025, Armenia
| | - Kostya S Novoselov
- National Graphene Institute, University of Manchester, Manchester M13 9PL, United Kingdom
- Institute for Functional Intelligent Materials, National University of Singapore, Building S9, 4 Science Drive 2, 117544, Singapore
- Chongqing 2D Materials Institute, Chongqing 400714, China
| | - Luis Martin-Moreno
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, 50009 Zaragoza, Spain
- Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain
| | - Valentyn S Volkov
- Emerging Technologies Research Center, XPANCEO, Dubai Investment Park First, Dubai 00000, United Arab Emirates
- Laboratory of Advanced Functional Materials, Yerevan State University, Yerevan 0025, Armenia
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Li J, Jia H, Zhou J, Huang X, Xu L, Jia S, Gao Z, Yao K, Li D, Zhang B, Liu Y, Huang Y, Hu Y, Zhao G, Xu Z, Li J, Yiu CK, Gao Y, Wu M, Jiao Y, Zhang Q, Tai X, Chan RH, Zhang Y, Ma X, Yu X. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat Commun 2023; 14:5009. [PMID: 37591881 PMCID: PMC10435523 DOI: 10.1038/s41467-023-40763-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 08/07/2023] [Indexed: 08/19/2023] Open
Abstract
Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.
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Affiliation(s)
- Jian Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Huiling Jia
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Jingkun Zhou
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Xingcan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Long Xu
- School of Mechanical and Aerospace Engineering, Jilin University, 130012, Changchun, China
| | - Shengxin Jia
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Zhan Gao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Kuanming Yao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Dengfeng Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Binbin Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yiming Liu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Ya Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yue Hu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Guangyao Zhao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zitong Xu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Jiyu Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Chun Ki Yiu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuyu Gao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Mengge Wu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), 610054, Chengdu, China
| | - Yanli Jiao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Qiang Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xuecheng Tai
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
| | - Raymond H Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuanting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Xiaohui Ma
- Department of vascular and endovascular surgery, The first medical center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China.
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
- City University of Hong Kong Shenzhen Research Institute, 518057, Shenzhen, China.
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35
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Li S, Wang H, Ma W, Qiu L, Xia K, Zhang Y, Lu H, Zhu M, Liang X, Wu XE, Liang H, Zhang Y. Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array. SCIENCE ADVANCES 2023; 9:eadh0615. [PMID: 37566652 PMCID: PMC10421034 DOI: 10.1126/sciadv.adh0615] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/11/2023] [Indexed: 08/13/2023]
Abstract
Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.
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Affiliation(s)
- Shuo Li
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Haomin Wang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Wei Ma
- Department of Cardiovascular Disease, Peking University First Hospital, Beijing 100084, PR China
| | - Lin Qiu
- Department of Cardiovascular Disease, Peking University First Hospital, Beijing 100084, PR China
| | - Kailun Xia
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Yong Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Haojie Lu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Mengjia Zhu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Xiaoping Liang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Xun-En Wu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Huarun Liang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Yingying Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
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36
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Ershad F, Patel S, Yu C. Wearable bioelectronics fabricated in situ on skins. NPJ FLEXIBLE ELECTRONICS 2023; 7:32. [PMID: 38665149 PMCID: PMC11041641 DOI: 10.1038/s41528-023-00265-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/04/2023] [Indexed: 04/28/2024]
Abstract
In recent years, wearable bioelectronics has rapidly expanded for diagnosing, monitoring, and treating various pathological conditions from the skin surface. Although the devices are typically prefabricated as soft patches for general usage, there is a growing need for devices that are customized in situ to provide accurate data and precise treatment. In this perspective, the state-of-the-art in situ fabricated wearable bioelectronics are summarized, focusing primarily on Drawn-on-Skin (DoS) bioelectronics and other in situ fabrication methods. The advantages and limitations of these technologies are evaluated and potential future directions are suggested for the widespread adoption of these technologies in everyday life.
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Affiliation(s)
- Faheem Ershad
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16801 USA
| | - Shubham Patel
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16801 USA
| | - Cunjiang Yu
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16801 USA
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16801 USA
- Department of Materials Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, PA 16801 USA
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37
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Grangeat P, Duval Comsa MP, Koenig A, Phlypo R. Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband. SENSORS (BASEL, SWITZERLAND) 2023; 23:6096. [PMID: 37447945 DOI: 10.3390/s23136096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/26/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023]
Abstract
The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device. The transport of carbon dioxide is modeled by convection-diffusion equations. We consider four compartments: blood, skin, the measurement cell and the collection cell. We introduce the state-space equations and the associated transition matrix associated with a Markovian model. We define an augmented system by combining a first-order autoregressive model describing the supply of carbon dioxide concentration in the blood compartment and its inertial resistance to change. We propose to use a Kalman filter to estimate the carbon dioxide concentration in the blood vessels recursively over time and thus monitor arterial carbon dioxide blood pressure in real time. Four performance factors with respect to the dynamic quantification of the CO2 blood concentration are considered, and a simulation is carried out based on data from a previous clinical study. These demonstrate the feasibility of such a technological concept.
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Affiliation(s)
- Pierre Grangeat
- CEA, Leti, MINATEC Campus, Université Grenoble Alpes, F-38000 Grenoble, France
| | | | - Anne Koenig
- CEA, Leti, MINATEC Campus, Université Grenoble Alpes, F-38000 Grenoble, France
| | - Ronald Phlypo
- CNRS, Grenoble INP, GIPSA-Lab, Université Grenoble Alpes, F-38000 Grenoble, France
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38
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Lin PH, Nien HH, Li BR. Wearable Microfluidics for Continuous Assay. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:181-203. [PMID: 36888989 DOI: 10.1146/annurev-anchem-091322-082930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The development of wearable devices provides approaches for the realization of self-health care. Easily carried wearable devices allow individual health monitoring at any place whenever necessary. There are various interesting monitoring targets, including body motion, organ pressure, and biomarkers. An efficient use of space in one small device is a promising resolution to increase the functions of wearable devices. Through integration of a microfluidic system into wearable devices, embedding complicated structures in one design becomes possible and can enable multifunction analyses within a limited device volume. This article reviews the reported microfluidic wearable devices, introduces applications to different biofluids, discusses characteristics of the design strategies and sensing principles, and highlights the attractive configurations of each device. This review seeks to provide a detailed summary of recent advanced microfluidic wearable devices. The overview of advanced key components is the basis for the development of future microfluidic wearable devices.
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Affiliation(s)
- Pei-Heng Lin
- Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan;
- Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hsin-Hua Nien
- Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan;
- College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Radiation Oncology, Cathay General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Bor-Ran Li
- Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan;
- Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Center for Emergent Functional Matter of Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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39
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Sel K, Mohammadi A, Pettigrew RI, Jafari R. Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation. NPJ Digit Med 2023; 6:110. [PMID: 37296218 DOI: 10.1038/s41746-023-00853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Amirmohammad Mohammadi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA.
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40
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Lin Z, Kireev D, Liu N, Gupta S, LaPiano J, Obaid SN, Chen Z, Akinwande D, Efimov IR. Graphene Biointerface for Cardiac Arrhythmia Diagnosis and Treatment. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2212190. [PMID: 36965107 DOI: 10.1002/adma.202212190] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/16/2023] [Indexed: 06/02/2023]
Abstract
Heart rhythm disorders, known as arrhythmias, cause significant morbidity and are one of the leading causes of mortality. Cardiac arrhythmias are frequently treated by implantable devices, such as pacemakers and defibrillators, or by ablation therapy guided by electroanatomical mapping. Both implantable and ablation therapies require sophisticated biointerfaces for electrophysiological measurements of electrograms and delivery of therapeutic stimulation or ablation energy. In this work, a graphene biointerface for in vivo cardiac electrophysiology is reported for the first time. Leveraging sub-micrometer-thick tissue-conformable graphene arrays, sensing and stimulation of the open mammalian heart are demonstrated both in vitro and in vivo. Furthermore, the graphene biointerface treatment of atrioventricular block (the kind of arrhythmia where the electrical conduction from the atria to the ventricles is interrupted) is demonstrated. The graphene arrays show effective electrochemical properties, namely interface impedance down to 40 Ω cm2 at 1 kHz, charge storage capacity up to 63.7 mC cm-2 , and charge injection capacity up to 704 µC cm-2 . Transparency of the graphene structures allows for simultaneous optical mapping of cardiac action potentials, calcium transients, and optogenetic stimulation while performing electrical measurements and stimulation. The report presents evidence of the significant potential of graphene biointerfaces for advanced cardiac electrophysiology and arrhythmia therapy.
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Affiliation(s)
- Zexu Lin
- Department of Biomedical Engineering, The George Washington University, Washington, DC, 20052, USA
| | - Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78758, USA
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA
| | - Ning Liu
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78758, USA
| | - Shubham Gupta
- Department of Biomedical Engineering, The George Washington University, Washington, DC, 20052, USA
| | - Jessica LaPiano
- MedStar Georgetown University Hospital, Washington, DC, 20007, USA
| | - Sofian N Obaid
- Department of Biomedical Engineering, The George Washington University, Washington, DC, 20052, USA
| | - Zhiyuan Chen
- Department of Biomedical Engineering, The George Washington University, Washington, DC, 20052, USA
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78758, USA
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA
| | - Igor R Efimov
- Department of Biomedical Engineering, The George Washington University, Washington, DC, 20052, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Medicine (Cardiology), Northwestern University, Chicago, IL, 60611, USA
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41
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Savchenko A, Kireev D, Yin RT, Efimov IR, Molokanova E. Graphene-based cardiac sensors and actuators. Front Bioeng Biotechnol 2023; 11:1168667. [PMID: 37256116 PMCID: PMC10225741 DOI: 10.3389/fbioe.2023.1168667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/12/2023] [Indexed: 06/01/2023] Open
Abstract
Graphene, a 2D carbon allotrope, is revolutionizing many biomedical applications due to its unique mechanical, electrical, thermal, and optical properties. When bioengineers realized that these properties could dramatically enhance the performance of cardiac sensors and actuators and may offer fundamentally novel technological capabilities, the field exploded with numerous studies developing new graphene-based systems and testing their limits. Here we will review the link between specific properties of graphene and mechanisms of action of cardiac sensors and actuators, analyze the performance of these systems from inaugural studies to the present, and offer future perspectives.
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Affiliation(s)
| | - Dmitry Kireev
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States
| | - Rose T. Yin
- Department of Biomedical Engineering, The George Washington University, Washington, DC, United States
| | - Igor R. Efimov
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Chicago, IL, United States
| | - Elena Molokanova
- Nanotools Bioscience, La Jolla, CA, United States
- NeurANO Bioscience, La Jolla, CA,United States
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42
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Castagnola V, Deleye L, Podestà A, Jaho E, Loiacono F, Debellis D, Trevisani M, Ciobanu DZ, Armirotti A, Pisani F, Flahaut E, Vazquez E, Bramini M, Cesca F, Benfenati F. Interactions of Graphene Oxide and Few-Layer Graphene with the Blood-Brain Barrier. NANO LETTERS 2023; 23:2981-2990. [PMID: 36917703 PMCID: PMC10103300 DOI: 10.1021/acs.nanolett.3c00377] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/03/2023] [Indexed: 06/18/2023]
Abstract
Thanks to their biocompatibility and high cargo capability, graphene-based materials (GRMs) might represent an ideal brain delivery system. The capability of GRMs to reach the brain has mainly been investigated in vivo and has highlighted some controversy. Herein, we employed two in vitro BBB models of increasing complexity to investigate the bionano interactions with graphene oxide (GO) and few-layer graphene (FLG): a 2D murine Transwell model, followed by a 3D human multicellular assembloid, to mimic the complexity of the in vivo architecture and intercellular crosstalk. We developed specific methodologies to assess the translocation of GO and FLG in a label-free fashion and a platform applicable to any nanomaterial. Overall, our results show good biocompatibility of the two GRMs, which did not impact the integrity and functionality of the barrier. Sufficiently dispersed subpopulations of GO and FLG were actively uptaken by endothelial cells; however, the translocation was identified as a rare event.
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Affiliation(s)
- Valentina Castagnola
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- IRCCS
Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
| | - Lieselot Deleye
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
| | - Alice Podestà
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
| | - Edra Jaho
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
| | - Fabrizio Loiacono
- IRCCS
Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
| | - Doriana Debellis
- Electron
Microscopy Facility, Istituto Italiano di
Tecnologia, Via Morego, 30, 16163 Genova, Italy
| | - Martina Trevisani
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- Department
of Experimental Medicine, Università
degli Studi di Genova, 16132 Genova, Italy
| | - Dinu Zinovie Ciobanu
- Analytical
Chemistry Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Andrea Armirotti
- Analytical
Chemistry Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Francesco Pisani
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- Department
of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Emmanuel Flahaut
- CIRIMAT,
UMR 5085, CNRS-INP-UPS, Université
Toulouse 3 Paul Sabatier, 118 route de Narbonne, F-31062 Toulouse cedex 9, France
| | - Ester Vazquez
- Instituto
Regional de Investigación Científica Aplicada (IRICA), Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain
- Facultad
de Ciencias y Tecnologías Químicas, Universidad de Castilla-La Mancha, Avda. Camilo José Cela S/N, 13071 Ciudad Real, Spain
| | - Mattia Bramini
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- Department
of Cell Biology, Universidad de Granada, C. Fuentenueva s/n, 18071 Granada, Spain
| | - Fabrizia Cesca
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- Department
of Life Sciences, University of Trieste, 34127, Trieste, Italy
| | - Fabio Benfenati
- Center
for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, Largo Rosanna Benzi 10, 16132 Genova, Italy
- IRCCS
Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
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43
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Sel K, Osman D, Huerta N, Edgar A, Pettigrew RI, Jafari R. Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device. NPJ Digit Med 2023; 6:59. [PMID: 36997608 PMCID: PMC10063561 DOI: 10.1038/s41746-023-00796-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 03/10/2023] [Indexed: 04/01/2023] Open
Abstract
Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89-213 mmHg and diastolic: 42-122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Deen Osman
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Noah Huerta
- Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA
| | - Arabella Edgar
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
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44
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Luo Y, Abidian MR, Ahn JH, Akinwande D, Andrews AM, Antonietti M, Bao Z, Berggren M, Berkey CA, Bettinger CJ, Chen J, Chen P, Cheng W, Cheng X, Choi SJ, Chortos A, Dagdeviren C, Dauskardt RH, Di CA, Dickey MD, Duan X, Facchetti A, Fan Z, Fang Y, Feng J, Feng X, Gao H, Gao W, Gong X, Guo CF, Guo X, Hartel MC, He Z, Ho JS, Hu Y, Huang Q, Huang Y, Huo F, Hussain MM, Javey A, Jeong U, Jiang C, Jiang X, Kang J, Karnaushenko D, Khademhosseini A, Kim DH, Kim ID, Kireev D, Kong L, Lee C, Lee NE, Lee PS, Lee TW, Li F, Li J, Liang C, Lim CT, Lin Y, Lipomi DJ, Liu J, Liu K, Liu N, Liu R, Liu Y, Liu Y, Liu Z, Liu Z, Loh XJ, Lu N, Lv Z, Magdassi S, Malliaras GG, Matsuhisa N, Nathan A, Niu S, Pan J, Pang C, Pei Q, Peng H, Qi D, Ren H, Rogers JA, Rowe A, Schmidt OG, Sekitani T, Seo DG, Shen G, Sheng X, Shi Q, Someya T, Song Y, Stavrinidou E, Su M, Sun X, Takei K, Tao XM, Tee BCK, Thean AVY, Trung TQ, Wan C, Wang H, Wang J, Wang M, Wang S, Wang T, Wang ZL, Weiss PS, Wen H, Xu S, Xu T, Yan H, Yan X, Yang H, Yang L, Yang S, Yin L, Yu C, Yu G, Yu J, Yu SH, Yu X, Zamburg E, Zhang H, Zhang X, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhao S, Zhao X, Zheng Y, Zheng YQ, Zheng Z, Zhou T, Zhu B, Zhu M, Zhu R, Zhu Y, Zhu Y, Zou G, Chen X. Technology Roadmap for Flexible Sensors. ACS NANO 2023; 17:5211-5295. [PMID: 36892156 PMCID: PMC11223676 DOI: 10.1021/acsnano.2c12606] [Citation(s) in RCA: 191] [Impact Index Per Article: 191.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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Affiliation(s)
- Yifei Luo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Centre for Flexible Devices (iFLEX), School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Mohammad Reza Abidian
- Department of Biomedical Engineering, University of Houston, Houston, Texas 77024, United States
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anne M Andrews
- Department of Chemistry and Biochemistry, California NanoSystems Institute, and Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Markus Antonietti
- Colloid Chemistry Department, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Campus Norrköping, Linköping University, 83 Linköping, Sweden
- Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC), SE-100 44 Stockholm, Sweden
| | - Christopher A Berkey
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Christopher John Bettinger
- Department of Biomedical Engineering and Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Peng Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Wenlong Cheng
- Nanobionics Group, Department of Chemical and Biological Engineering, Monash University, Clayton, Australia, 3800
- Monash Institute of Medical Engineering, Monash University, Clayton, Australia3800
| | - Xu Cheng
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Seon-Jin Choi
- Division of Materials of Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Alex Chortos
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Reinhold H Dauskardt
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Chong-An Di
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Michael D Dickey
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Xiangfeng Duan
- Department of Chemistry and Biochemistry, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Antonio Facchetti
- Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, Illinois 60208, United States
| | - Zhiyong Fan
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Yin Fang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Jianyou Feng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Xue Feng
- Laboratory of Flexible Electronics Technology, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Huajian Gao
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California, 91125, United States
| | - Xiwen Gong
- Department of Chemical Engineering, Department of Materials Science and Engineering, Department of Electrical Engineering and Computer Science, Applied Physics Program, and Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, Michigan, 48109 United States
| | - Chuan Fei Guo
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiaojun Guo
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Martin C Hartel
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Zihan He
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - John S Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
| | - Youfan Hu
- School of Electronics and Center for Carbon-Based Electronics, Peking University, Beijing 100871, China
| | - Qiyao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Yu Huang
- Department of Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Fengwei Huo
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, PR China
| | - Muhammad M Hussain
- mmh Labs, Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Ali Javey
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Unyong Jeong
- Department of Materials Science and Engineering, Pohang University of Science and Engineering (POSTECH), Pohang, Gyeong-buk 37673, Korea
| | - Chen Jiang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Xingyu Jiang
- Department of Biomedical Engineering, Southern University of Science and Technology, No 1088, Xueyuan Road, Xili, Nanshan District, Shenzhen, Guangdong 518055, PR China
| | - Jiheong Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daniil Karnaushenko
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
| | | | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Il-Doo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Lingxuan Kong
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
- NUS Graduate School-Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Nae-Eung Lee
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Pooi See Lee
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Singapore-HUJ Alliance for Research and Enterprise (SHARE), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Seoul National University, Soft Foundry, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Fengyu Li
- College of Chemistry and Materials Science, Jinan University, Guangzhou, Guangdong 510632, China
| | - Jinxing Li
- Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Neuroscience Program, BioMolecular Science Program, and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48823, United States
| | - Cuiyuan Liang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
- Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 119276, Singapore
| | - Yuanjing Lin
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Darren J Lipomi
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093-0448, United States
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Kai Liu
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Nan Liu
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing 100875, PR China
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Yuxin Liu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Biomedical Engineering, N.1 Institute for Health, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore
| | - Yuxuan Liu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Zhiyuan Liu
- Neural Engineering Centre, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 518055
| | - Zhuangjian Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhisheng Lv
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Shlomo Magdassi
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge CB3 0FA, Cambridge United Kingdom
| | - Naoji Matsuhisa
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge CB3 9EU, United Kingdom
| | - Simiao Niu
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Changhyun Pang
- School of Chemical Engineering and Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Qibing Pei
- Department of Materials Science and Engineering, Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Dianpeng Qi
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Huaying Ren
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, 90095, United States
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, Illinois 60208, United States
- Department of Materials Science and Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Departments of Electrical and Computer Engineering and Chemistry, and Department of Neurological Surgery, Northwestern University, Evanston, Illinois 60208, United States
| | - Aaron Rowe
- Becton, Dickinson and Company, 1268 N. Lakeview Avenue, Anaheim, California 92807, United States
- Ready, Set, Food! 15821 Ventura Blvd #450, Encino, California 91436, United States
| | - Oliver G Schmidt
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
- Material Systems for Nanoelectronics, Chemnitz University of Technology, Chemnitz 09107, Germany
- Nanophysics, Faculty of Physics, TU Dresden, Dresden 01062, Germany
| | - Tsuyoshi Sekitani
- The Institute of Scientific and Industrial Research (SANKEN), Osaka University, Osaka, Japan 5670047
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Center for Flexible Electronics Technology, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Qiongfeng Shi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
| | - Takao Someya
- Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yanlin Song
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Eleni Stavrinidou
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-601 74 Norrkoping, Sweden
| | - Meng Su
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Xuemei Sun
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Kuniharu Takei
- Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Xiao-Ming Tao
- Research Institute for Intelligent Wearable Systems, School of Fashion and Textiles, Hong Kong Polytechnic University, Hong Kong, China
| | - Benjamin C K Tee
- Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- iHealthtech, National University of Singapore, Singapore 119276, Singapore
| | - Aaron Voon-Yew Thean
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Tran Quang Trung
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Huiliang Wang
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Joseph Wang
- Department of Nanoengineering, University of California, San Diego, California 92093, United States
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chip and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China
- the Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No.701 Yunjin Road, Xuhui District, Shanghai 200232, China
| | - Sihong Wang
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, 60637, United States
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays and Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Georgia Institute of Technology, Atlanta, Georgia 30332-0245, United States
| | - Paul S Weiss
- California NanoSystems Institute, Department of Chemistry and Biochemistry, Department of Bioengineering, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Hanqi Wen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
- Institute of Flexible Electronics Technology of THU, Jiaxing, Zhejiang, China 314000
| | - Sheng Xu
- Department of Nanoengineering, Department of Electrical and Computer Engineering, Materials Science and Engineering Program, and Department of Bioengineering, University of California San Diego, La Jolla, California, 92093, United States
| | - Tailin Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, PR China
| | - Hongping Yan
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Xuzhou Yan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Hui Yang
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, China, 300072
| | - Le Yang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Materials Science and Engineering, National University of Singapore (NUS), 9 Engineering Drive 1, #03-09 EA, Singapore 117575, Singapore
| | - Shuaijian Yang
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, and Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Cunjiang Yu
- Department of Engineering Science and Mechanics, Department of Biomedical Engineering, Department of Material Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, United States
| | - Guihua Yu
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States
| | - Jing Yu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shu-Hong Yu
- Department of Chemistry, Institute of Biomimetic Materials and Chemistry, Hefei National Research Center for Physical Science at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Evgeny Zamburg
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Haixia Zhang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xueji Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, PR China
| | - Yihui Zhang
- Applied Mechanics Laboratory, Department of Engineering Mechanics; Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Yu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Siyuan Zhao
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Xuanhe Zhao
- Department of Mechanical Engineering, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Yuanjin Zheng
- Center for Integrated Circuits and Systems, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yu-Qing Zheng
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Zijian Zheng
- Department of Applied Biology and Chemical Technology, Faculty of Science, Research Institute for Intelligent Wearable Systems, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Tao Zhou
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Huck Institutes of the Life Sciences, Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Ming Zhu
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
| | - Rong Zhu
- Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, California, 90064, United States
| | - Yong Zhu
- Department of Mechanical and Aerospace Engineering, Department of Materials Science and Engineering, and Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Guijin Zou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xiaodong Chen
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Laboratory for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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45
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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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46
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Zhou ZB, Cui TR, Li D, Jian JM, Li Z, Ji SR, Li X, Xu JD, Liu HF, Yang Y, Ren TL. Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16062133. [PMID: 36984013 PMCID: PMC10057755 DOI: 10.3390/ma16062133] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.
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Affiliation(s)
- Zi-Bo Zhou
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
| | - Tian-Rui Cui
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Ding Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jin-Ming Jian
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Zhen Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Shou-Rui Ji
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xin Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jian-Dong Xu
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Hou-Fang Liu
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yi Yang
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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47
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Seo S, Jo H, Kim J, Lee B, Bien F. An ultralow power wearable vital sign sensor using an electromagnetically reactive near field. Bioeng Transl Med 2023; 8:e10502. [DOI: 10.1002/btm2.10502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/31/2022] [Accepted: 02/12/2023] [Indexed: 03/01/2023] Open
Affiliation(s)
- Seoktae Seo
- Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Hyunkyeong Jo
- Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Jungho Kim
- Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Bonyoung Lee
- Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Franklin Bien
- Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea
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48
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Ismail SNA, Nayan NA, Mohammad Haniff MAS, Jaafar R, May Z. Wearable Two-Dimensional Nanomaterial-Based Flexible Sensors for Blood Pressure Monitoring: A Review. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:852. [PMID: 36903730 PMCID: PMC10005058 DOI: 10.3390/nano13050852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.
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Affiliation(s)
- Siti Nor Ashikin Ismail
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia
| | - Nazrul Anuar Nayan
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia
- Institute Islam Hadhari, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia
| | | | - Rosmina Jaafar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia
| | - Zazilah May
- Electrical and Electronic Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, Malaysia
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49
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Pang J, Peng S, Hou C, Zhao H, Fan Y, Ye C, Zhang N, Wang T, Cao Y, Zhou W, Sun D, Wang K, Rümmeli MH, Liu H, Cuniberti G. Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles. ACS Sens 2023; 8:482-514. [PMID: 36656873 DOI: 10.1021/acssensors.2c02790] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.
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Affiliation(s)
- Jinbo Pang
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan, Jinan 250022, China
| | - Songang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center and Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Chongyang Hou
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan, Jinan 250022, China
| | - Hongbin Zhao
- State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co. Ltd., Xinwai Street 2, Beijing 100088, People's Republic of China
| | - Yingju Fan
- School of Chemistry and Chemical Engineering, University of Jinan, Shandong, Jinan 250022, China
| | - Chen Ye
- School of Chemistry and Chemical Engineering, University of Jinan, Shandong, Jinan 250022, China
| | - Nuo Zhang
- School of Chemistry and Chemical Engineering, University of Jinan, Shandong, Jinan 250022, China
| | - Ting Wang
- State Key Laboratory of Biobased Material and Green Papermaking and People's Republic of China School of Bioengineering, Qilu University of Technology, Shandong Academy of Sciences, No. 3501 Daxue Road, Jinan 250353, People's Republic of China
| | - Yu Cao
- Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology (Ministry of Education) and School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Weijia Zhou
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan, Jinan 250022, China
| | - Ding Sun
- School of Electrical and Computer Engineering, Jilin Jianzhu University, Changchun 130118, P. R. China
| | - Kai Wang
- School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
| | - Mark H Rümmeli
- Leibniz Institute for Solid State and Materials Research Dresden, Dresden, D-01171, Germany.,College of Energy, Soochow Institute for Energy and Materials Innovations, and Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, Soochow University, Suzhou 215006, China.,Centre of Polymer and Carbon Materials, Polish Academy of Sciences, M. Curie Sklodowskiej 34, Zabrze 41-819, Poland.,Institute for Complex Materials, IFW Dresden, 20 Helmholtz Strasse, Dresden 01069, Germany.,Center for Energy and Environmental Technologies, VŠB-Technical University of Ostrava, 17. Listopadu 15, Ostrava 708 33, Czech Republic
| | - Hong Liu
- Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan, Jinan 250022, China.,State Key Laboratory of Crystal Materials, Center of Bio & Micro/Nano Functional Materials, Shandong University, 27 Shandanan Road, Jinan 250100, China
| | - Gianaurelio Cuniberti
- Institute for Materials Science and Max Bergmann Center of Biomaterials and Center for Advancing Electronics Dresden, Technische Universität Dresden, Dresden 01069, Germany
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50
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Deng Z, Guo L, Chen X, Wu W. Smart Wearable Systems for Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052479. [PMID: 36904682 PMCID: PMC10007426 DOI: 10.3390/s23052479] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration, and the number of high-performance wearable systems has been gradually increasing in recent years. However, there are still many challenges in these fields, such as balancing the trade-off between flexibility/stretchability, sensing performance, and the robustness of systems. For this reason, more evolution is required to promote the development of wearable health-monitoring systems. In this regard, this review summarizes some representative achievements and recent progress of wearable systems for health monitoring. Meanwhile, a strategy overview is presented about selecting materials, integrating systems, and monitoring biosignals. The next generation of wearable systems for accurate, portable, continuous, and long-term health monitoring will offer more opportunities for disease diagnosis and treatment.
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Affiliation(s)
- Zhiyong Deng
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
- Nuclear Power Institute of China, Huayang, Shuangliu District, Chengdu 610213, China
| | - Lihao Guo
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Ximeng Chen
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
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