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Tran ATT, Hassan K, Tung TT, Tripathy A, Mondal A, Losic D. Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation. NANOSCALE 2024. [PMID: 38644676 DOI: 10.1039/d4nr00174e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.
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Affiliation(s)
- Anh Tuan Trong Tran
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia, Australia.
| | - Kamrul Hassan
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia, Australia.
| | - Tran Thanh Tung
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia, Australia.
| | - Ashis Tripathy
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vandalur-Kelambakkam Road, Chennai 600127, India
| | - Ashok Mondal
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vandalur-Kelambakkam Road, Chennai 600127, India
| | - Dusan Losic
- School of Chemical Engineering, The University of Adelaide, Adelaide, South Australia, Australia.
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2
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Wang Y, Xu H, Sui B, Xi H, Fu Y, Zhao W, Li P, Sun S, Wang D, Zhang J. Self-aspiration sampling design for rapid analyses of volatile organic compounds based on atmospheric pressure chemical ionization/photoionization combined ionization source mass spectrometry. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1763-1769. [PMID: 38450684 DOI: 10.1039/d4ay00019f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Development of combined mass spectrometry ionization sources has enabled expansion of the application and scope of mass spectrometry. A novel hybrid ionization system combining vacuum ultraviolet (VUV) and atmospheric pressure chemical ionization (APCI) was constructed. Gaseous samples were self-aspirated into an ionization zone through a capillary by negative pressure, generated by high-speed airflow based on the Venturi effect. Compared with APCI mode alone, the signal-to-noise ratio (S/N) in APCI/VUV mode was increased by about 276-times. To increase the ionization efficiency further, correlated experimental conditions were optimized. Four types of volatile organic compounds (VOCs) were tested to evaluate the performance of the APCI/VUV ion source. Excellent linearity and limit of detection were achieved for compounds in mixed solutions. Quantitative analyses of four VOCs (toluene, cyclohexanone, styrene and ethylbenzene) using APCI/VUV-MS were done, and the relative standard deviations (RSDs) were 1.57%, 6.30%, 4.49% and 8.21%, respectively, indicating that the APCI/VUV ionization source had excellent reproducibility. Our results demonstrated that the developed method was promising for analyzing VOCs as well as being rapid, simple, and easy to operate.
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Affiliation(s)
- Yuxin Wang
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Hengyi Xu
- Technology Center, China Tobacco Shenzhen Tobacco Industrial Co., Ltd., Guangdong, 518110, China
| | - Bo Sui
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Hui Xi
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Yingjie Fu
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Wuduo Zhao
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Center of Advanced Analysis and Gene Sequencing, Zhengzhou University, Zhengzhou, 450001, China
- Food Laboratory of Zhongyuan, Luohe 462000, P. R. China
| | - Peng Li
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Shihao Sun
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
- Food Laboratory of Zhongyuan, Luohe 462000, P. R. China
| | - Dingzhong Wang
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Key Laboratory of Tobacco Flavor Basic Research of CNTC, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
| | - Jianxun Zhang
- Flavor Science Research Center, College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
- Food Laboratory of Zhongyuan, Luohe 462000, P. R. China
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Xue X, Wei M, Yuan J, Huang X, Cao Q, Xia C, Niu X, Yin X. A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123470. [PMID: 37776834 DOI: 10.1016/j.saa.2023.123470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/17/2023] [Accepted: 09/26/2023] [Indexed: 10/02/2023]
Abstract
A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.
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Affiliation(s)
- Xiangfen Xue
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Mingjie Wei
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jing Yuan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Xinyu Huang
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Qinghua Cao
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Changkun Xia
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Xiangheng Niu
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Xiulian Yin
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China.
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Moura PC, Ribeiro PA, Raposo M, Vassilenko V. The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers. SENSORS (BASEL, SWITZERLAND) 2023; 23:9271. [PMID: 38005657 PMCID: PMC10674474 DOI: 10.3390/s23229271] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023]
Abstract
The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.
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Affiliation(s)
| | | | | | - Valentina Vassilenko
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-NOVA), Department of Physics, NOVA School of Science and Technology, NOVA University of Lisbon, Campus FCT-NOVA, 2829-516 Caparica, Portugal; (P.C.M.); (P.A.R.); (M.R.)
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5
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Devi M, Wang H, Moon S, Sharma S, Strauss V. Laser-Carbonization - A Powerful Tool for Micro-Fabrication of Patterned Electronic Carbons. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211054. [PMID: 36841955 DOI: 10.1002/adma.202211054] [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: 11/27/2022] [Revised: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Fabricating electronic devices from natural, renewable resources is a common goal in engineering and materials science. In this regard, carbon is of special significance due to its biocompatibility combined with electrical conductivity and electrochemical stability. In microelectronics, however, carbon's device application is often inhibited by tedious and expensive preparation processes and a lack of control over processing and material parameters. Laser-assisted carbonization is emerging as a tool for the precise and selective synthesis of functional carbon-based materials for flexible device applications. In contrast to conventional carbonization via in-furnace pyrolysis, laser-carbonization is induced photo-thermally and occurs on the time-scale of milliseconds. By careful selection of the precursors and process parameters, the properties of this so-called laser-patterned carbon (LP-C) such as porosity, surface polarity, functional groups, degree of graphitization, charge-carrier structure, etc. can be tuned. In this critical review, a common perspective is generated on laser-carbonization in the context of general carbonization strategies, fundamentals of laser-induced materials processing, and flexible electronic applications, like electrodes for sensors, electrocatalysts, energy storage, or antennas. An attempt is made to have equal emphasis on material processing and application aspects such that this emerging technology can be optimally positioned in the broader context of carbon-based microfabrication.
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Affiliation(s)
- Mamta Devi
- School of Mechanical and Materials Engineering, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175075, India
| | - Huize Wang
- Department Kolloidchemie, Max-Planck-Institut für Kolloid- und Grenzflächenforschung, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Sanghwa Moon
- Department Kolloidchemie, Max-Planck-Institut für Kolloid- und Grenzflächenforschung, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Swati Sharma
- School of Mechanical and Materials Engineering, Indian Institute of Technology Mandi, Kamand, Himachal Pradesh, 175075, India
| | - Volker Strauss
- Department Kolloidchemie, Max-Planck-Institut für Kolloid- und Grenzflächenforschung, Am Mühlenberg 1, 14476, Potsdam, Germany
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6
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Zhou K, Kammarchedu V, Butler D, Soltan Khamsi P, Ebrahimi A. Electrochemical Sensors Based on MoS x -Functionalized Laser-Induced Graphene for Real-Time Monitoring of Phenazines Produced by Pseudomonas aeruginosa. Adv Healthc Mater 2022; 11:e2200773. [PMID: 35853169 PMCID: PMC9547893 DOI: 10.1002/adhm.202200773] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/21/2022] [Indexed: 01/27/2023]
Abstract
Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen causing infections in blood and implanted devices. Traditional identification methods take more than 24 h to produce results. Molecular biology methods expedite detection, but require an advanced skill set. To address these challenges, this work demonstrates functionalization of laser-induced graphene (LIG) for developing flexible electrochemical sensors for P. aeruginosa based on phenazines. Electrodeposition as a facile approach is used to functionalize LIG with molybdenum polysulfide (MoSx ). The sensor's limit of detection (LOD), sensitivity, and specificity are determined in broth, agar, and wound simulating medium (WSM). Control experiments with Escherichia coli, which does not produce phenazines, demonstrate specificity of sensors for P. aeruginosa. The LOD for pyocyanin (PYO) and phenazine-1-carboxylic acid (PCA) is 0.19 × 10-6 and 1.2 × 10-6 m, respectively. Furthermore, the highly stable sensors enable real-time monitoring of P. aeruginosa biofilms over several days. Comparing square wave voltammetry data over time shows time-dependent generation of phenazines. In particular, two configurations-"Normal" and "Flipped"-are studied, showing that the phenazines time dynamics vary depending on how cells interact with sensors. The reported results demonstrate the potential of the developed sensors for integration with wound dressings for early diagnosis of P. aeruginosa infection.
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Affiliation(s)
- Keren Zhou
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA
| | - Vinay Kammarchedu
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA
| | - Derrick Butler
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA
| | - Pouya Soltan Khamsi
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA
| | - Aida Ebrahimi
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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7
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Li D, Liu W, Zhu B, Qu M, Zhang Q, Fu Y, Xie J. Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever. ACS Sens 2022; 7:2767-2777. [PMID: 36106454 DOI: 10.1021/acssensors.2c01423] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS2 film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm2) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS2 after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO2 concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO2 concentrations for various applications, revealing its great potential in human-machine interactions and health monitoring of plants and human beings.
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Affiliation(s)
- Dongsheng Li
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
| | - Weiting Liu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
| | - Boyi Zhu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
| | - Mengjiao Qu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
| | - Qian Zhang
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
| | - YongQing Fu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, U.K
| | - Jin Xie
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China
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Vernon J, Canyelles-Pericas P, Torun H, Binns R, Ng WP, Wu Q, Fu YQ. Breath monitoring, sleep disorder detection, and tracking using thin-film acoustic waves and open-source electronics. NANOTECHNOLOGY AND PRECISION ENGINEERING 2022. [DOI: 10.1063/10.0013471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.
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Affiliation(s)
- Jethro Vernon
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Pep Canyelles-Pericas
- Department of Integrated Devices and Systems, MESA+ Institute for Nanotechnology, University of Twente, Enschede 7522 NB, The Netherlands
| | - Hamdi Torun
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Richard Binns
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Wai Pang Ng
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Qiang Wu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Yong-Qing Fu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
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Bhargava Reddy MS, Kailasa S, Marupalli BCG, Sadasivuni KK, Aich S. A Family of 2D-MXenes: Synthesis, Properties, and Gas Sensing Applications. ACS Sens 2022; 7:2132-2163. [PMID: 35972775 DOI: 10.1021/acssensors.2c01046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Gas sensors, capable of detecting and monitoring trace amounts of gas molecules or volatile organic compounds (VOCs), are in great demand for numerous applications including diagnosing diseases through breath analysis, environmental and personal safety, food and agriculture, and other fields. The continuous emergence of new materials is one of the driving forces for the development of gas sensors. Recently, 2D materials have been gaining huge attention for gas sensing applications, owing to their superior electrical, optical, and mechanical characteristics. Especially for 2D MXenes, high specific area and their rich surface functionalities with tunable electronic structure make them compelling for sensing applications. This Review discusses the latest advancements in the 2D MXenes for gas sensing applications. It starts by briefly explaining the family of MXenes, their synthesis methods, and delamination procedures. Subsequently, it outlines the properties of MXenes. Then it describes the theoretical and experimental aspects of the MXenes-based gas sensors. Discussion is also extended to the relation between sensing performance and the structure, electronic properties, and surface chemistry. Moreover, it highlights the promising potential of these materials in the current gas sensing applications and finally it concludes with the limitations, challenges, and future prospects of 2D MXenes in gas sensing applications.
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Affiliation(s)
- M Sai Bhargava Reddy
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Saraswathi Kailasa
- Department of Physics, National Institute of Technology, Warangal, 506004, India
| | - Bharat C G Marupalli
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | | | - Shampa Aich
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
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10
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Reddy YVM, Shin JH, Palakollu VN, Sravani B, Choi CH, Park K, Kim SK, Madhavi G, Park JP, Shetti NP. Strategies, advances, and challenges associated with the use of graphene-based nanocomposites for electrochemical biosensors. Adv Colloid Interface Sci 2022; 304:102664. [PMID: 35413509 DOI: 10.1016/j.cis.2022.102664] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/18/2022] [Accepted: 04/04/2022] [Indexed: 12/29/2022]
Abstract
Graphene is an intriguing two-dimensional honeycomb-like carbon material with a unique basal plane structure, charge carrier mobility, thermal conductivity, wide electrochemical spectrum, and unusual physicochemical properties. Therefore, it has attracted considerable scientific interest in the field of nanoscience and bionanotechnology. The high specific surface area of graphene allows it to support high biomolecule loading for good detection sensitivity. As such, graphene, graphene oxide (GO), and reduced GO are excellent materials for the fabrication of new nanocomposites and electrochemical sensors. Graphene has been widely used as a chemical building block and/or scaffold with various materials to create highly sensitive and selective electrochemical sensing microdevices. Over the past decade, significant advancements have been made by utilizing graphene and graphene-based nanocomposites to design electrochemical sensors with enhanced analytical performance. This review focus on the synthetic strategies, as well as the structure-to-function studies of graphene, electrochemistry, novel multi nanocomposites combining graphene, limit of detection, stability, sensitivity, assay time. Finally, the review describes the challenges, strategies and outlook on the future development of graphene sensors technology that would be usable for the internet of things are also highlighted.
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Li D, Zhu B, Pang K, Zhang Q, Qu M, Liu W, Fu Y, Xie J. Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds. ACS Sens 2022; 7:1555-1563. [PMID: 35549157 DOI: 10.1021/acssensors.2c00442] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.
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Affiliation(s)
- Dongsheng Li
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
| | - Boyi Zhu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
| | - Kai Pang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, People’s Republic of China
| | - Qian Zhang
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
| | - Mengjiao Qu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
| | - Weiting Liu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
| | - YongQing Fu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Jin Xie
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, People’s Republic of China
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Zhao Q, Jiang Y, Yuan Z, Duan Z, Zhang Y, Tai H. MXene复合气敏材料: 最新进展与未来挑战. CHINESE SCIENCE BULLETIN-CHINESE 2022. [DOI: 10.1360/tb-2021-1340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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13
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Wasilewski T, Brito NF, Szulczyński B, Wojciechowski M, Buda N, Melo ACA, Kamysz W, Gębicki J. Olfactory Receptor-based Biosensors as Potential Future Tools in Medical Diagnosis. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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14
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Zhang H, He R, Niu Y, Han F, Li J, Zhang X, Xu F. Graphene-enabled wearable sensors for healthcare monitoring. Biosens Bioelectron 2022; 197:113777. [PMID: 34781177 DOI: 10.1016/j.bios.2021.113777] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 01/19/2023]
Abstract
Wearable sensors in healthcare monitoring have recently found widespread applications in biomedical fields for their non- or minimal-invasive, user-friendly and easy-accessible features. Sensing materials is one of the major challenges to achieve these superiorities of wearable sensors for healthcare monitoring, while graphene-based materials with many favorable properties have shown great efficiency in sensing various biochemical and biophysical signals. In this paper, we review state-of-the-art advances in the development and modification of graphene-based materials (i.e., graphene, graphene oxide and reduced graphene oxide) for fabricating advanced wearable sensors with 1D (fibers), 2D (films) and 3D (foams/aerogels/hydrogels) macroscopic structures. We summarize the structural design guidelines, sensing mechanisms, applications and evolution of the graphene-based materials as wearable sensors for healthcare monitoring of biophysical signals (e.g., mechanical, thermal and electrophysiological signals) and biochemical signals from various body fluids and exhaled gases. Finally, existing challenges and future prospects are presented in this area.
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Affiliation(s)
- Huiqing Zhang
- Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education, School of Energy & Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; The Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, China
| | - Rongyan He
- The Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yan Niu
- The Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, China
| | - Fei Han
- The Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jing Li
- Department of Plastic and Burn Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, China
| | - Xiongwen Zhang
- Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education, School of Energy & Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, China.
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Cova CM, Rincón E, Espinosa E, Serrano L, Zuliani A. Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs). BIOSENSORS 2022; 12:51. [PMID: 35200311 PMCID: PMC8869180 DOI: 10.3390/bios12020051] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/13/2022] [Accepted: 01/18/2022] [Indexed: 05/06/2023]
Abstract
The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.
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Affiliation(s)
- Camilla Maria Cova
- Department of Chemistry, University of Florence and CSGI, Via della Lastruccia 3, 50019 Sesto Fiorentino, FI, Italy;
| | - Esther Rincón
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Eduardo Espinosa
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Luis Serrano
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Alessio Zuliani
- Department of Chemistry, University of Florence and CSGI, Via della Lastruccia 3, 50019 Sesto Fiorentino, FI, Italy;
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Brooks AK, Chakravarty S, Yadavalli VK. Flexible Sensing Systems for Cancer Diagnostics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1379:275-306. [DOI: 10.1007/978-3-031-04039-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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17
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Li D, Xie Z, Qu M, Zhang Q, Fu Y, Xie J. Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds. ACS APPLIED MATERIALS & INTERFACES 2021; 13:47043-47051. [PMID: 34546706 DOI: 10.1021/acsami.1c13046] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify "patients with diabetic ketosis" from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis.
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Affiliation(s)
- Dongsheng Li
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Zihao Xie
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Mengjiao Qu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Qian Zhang
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yongqing Fu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, U.K
| | - Jin Xie
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang 310027, China
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