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Chisanga M, Masson JF. Machine Learning-Driven SERS Nanoendoscopy and Optophysiology. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2024; 17:313-338. [PMID: 38701442 DOI: 10.1146/annurev-anchem-061622-012448] [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: 05/05/2024]
Abstract
A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.
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Affiliation(s)
- Malama Chisanga
- Département de Chimie, Institut Courtois, Quebec Center for Advanced Materials, Regroupement Québécois sur les Matériaux de Pointe, and Centre Interdisciplinaire de Recherche sur le Cerveau et l'Apprentissage, Université de Montréal, Montréal, Québec, Canada;
| | - Jean-Francois Masson
- Département de Chimie, Institut Courtois, Quebec Center for Advanced Materials, Regroupement Québécois sur les Matériaux de Pointe, and Centre Interdisciplinaire de Recherche sur le Cerveau et l'Apprentissage, Université de Montréal, Montréal, Québec, Canada;
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2
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Zhang J, Qian C, You G, Wang T, Saifullah Y, Abdi-Ghaleh R, Chen H. Harnessing the Missing Spectral Correlation for Metasurface Inverse Design. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2308807. [PMID: 38946621 DOI: 10.1002/advs.202308807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/26/2024] [Indexed: 07/02/2024]
Abstract
A long-held tenet in computer science asserts that the training of deep learning is analogous to an alchemical furnace, and its "black box" signature brings forth inexplicability. For electromagnetic metasurfaces, the related intelligent applications also get stuck into such a dilemma. Although the past 5 years have witnessed a proliferation of deep learning-based works across complex photonic scenarios, they neglect the already existing but untapped physical laws. Here, the intrinsic correlation between the real and imaginary parts of the spectra are revealed using Kramers-Kronig relations, which is then mimicked by bidirectional information flow in neural network space. Such consideration harnesses the missing spectral connection to extract crucial features effectively. The bidirectional recurrent neural network is benchmarked in metasurface inverse design and compare it with a fully-connected neural network, unidirectional recurrent neural network, and attention-based transformer. Beyond the improved accuracy, the study examines the intermediate information products and physically explains why different network structures yield different performances. The work offers explicable perspectives to utilize physical information in the deep learning field and facilitates many data-intensive research endeavors.
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Affiliation(s)
- Jie Zhang
- ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University, Hangzhou, 310027, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, 321099, China
| | - Chao Qian
- ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University, Hangzhou, 310027, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, 321099, China
| | - Guangfeng You
- ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University, Hangzhou, 310027, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, 321099, China
| | - Tao Wang
- State Key Laboratory of Integrated Service Networks, Xidian University, Xian, 710071, China
| | - Yasir Saifullah
- ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University, Hangzhou, 310027, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, 321099, China
| | - Reza Abdi-Ghaleh
- Department of Laser and Optical Engineering, University of Bonab, Bonab, 5551395133, Iran
| | - Hongsheng Chen
- ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, Zhejiang University, Hangzhou, 310027, China
- Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, 321099, China
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3
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O'Dell ZJ, Knobeloch M, Skrabalak SE, Willets KA. High-Throughput All-Optical Determination of Nanorod Size and Orientation. NANO LETTERS 2024. [PMID: 38848456 DOI: 10.1021/acs.nanolett.4c01261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
As a single-particle characterization technique, optical microscopy has transformed our understanding of structure-function relationships of plasmonic nanoparticles, but the need for ex-situ-correlated electron microscopy to obtain structural information handicaps an otherwise exceptional high-throughput technique. Here, we present an all-optical alternative to electron microscopy to accurately and quickly extract structural information about single gold nanorods (Au NRs) using calcite-assisted localization and kinetics (CLocK) microscopy. Color CLocK images of single Au NRs allow scattering from the longitudinal and transverse plasmon modes to be imaged simultaneously, encoding spectral data in CLocK images that can then be extracted to obtain Au NR size and orientation. Moreover, through the use of convolutional neural networks, Au NR length, width, and aspect ratio can be predicted directly from color CLocK images within ∼10% of the true value measured by electron microscopy.
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Affiliation(s)
- Zachary J O'Dell
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Megan Knobeloch
- Department of Chemistry, Indiana University-Bloomington, Bloomington, Indiana 47405, United States
| | - Sara E Skrabalak
- Department of Chemistry, Indiana University-Bloomington, Bloomington, Indiana 47405, United States
| | - Katherine A Willets
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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4
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Pathak A, Verma N, Tripathi S, Mishra A, Poluri KM. Nanosensor based approaches for quantitative detection of heparin. Talanta 2024; 273:125873. [PMID: 38460425 DOI: 10.1016/j.talanta.2024.125873] [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: 12/25/2023] [Revised: 02/23/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
Heparin, being a widely employed anticoagulant in numerus clinical complications, requires strict quantification and qualitative screening to ensure the safety of patients from potential threat of thrombocytopenia. However, the intricacy of heparin's chemical structures and low abundance hinders the precise monitoring of its level and quality in clinical settings. Conventional laboratory assays have limitations in sensitivity and specificity, necessitating the development of innovative approaches. In this context, nanosensors emerged as a promising solution due to enhanced sensitivity, selectivity, and ability to detect heparin even at low concentrations. This review delves into a range of sensing approaches including colorimetric, fluorometric, surface-enhanced Raman spectroscopy, and electrochemical techniques using different types of nanomaterials, thus providing insights of its principles, capabilities, and limitations. Moreover, integration of smart-phone with nanosensors for point of care diagnostics has also been explored. Additionally, recent advances in nanopore technologies, artificial intelligence (AI) and machine learning (ML) have been discussed offering specificity against contaminants present in heparin to ensure its quality. By consolidating current knowledge and highlighting the potential of nanosensors, this review aims to contribute to the advancement of efficient, reliable, and economical heparin detection methods providing improved patient care.
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Affiliation(s)
- Aakanksha Pathak
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Nishchay Verma
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Shweta Tripathi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Amit Mishra
- Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Jodhpur, 342011, Rajasthan, India
| | - Krishna Mohan Poluri
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India; Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.
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5
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Feng Y, Yang X, Rao Q, Zhang L, Su Y, Lv Y. Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite. Anal Chem 2024; 96:7240-7247. [PMID: 38661330 DOI: 10.1021/acs.analchem.4c00899] [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: 04/26/2024]
Abstract
In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its application for precise biosensing owing to tissue absorption and scattering. In this work, a PersL lifetime-based nanoplatform via deep learning was proposed for high-fidelity bioimaging and biosensing in vivo. The persistent luminescence imaging network (PLI-Net), which consisted of a 3D-deep convolutional neural network (3D-CNN) and the PersL imaging system, was logically constructed to accurately extract the lifetime feature from the profile of PersL intensity-based decay images. Significantly, the NIR PersL nanomaterials represented by Zn1+xGa2-2xSnxO4: 0.4 % Cr (ZGSO) were precisely adjusted over their lifetime, enabling the PersL lifetime-based imaging with high-contrast signals. Inspired by the adjustable and reliable PersL lifetime imaging of ZGSO NPs, a proof-of-concept PersL nanoplatform was further developed and showed exceptional analytical performance for hypochlorite detection via a luminescence resonance energy transfer process. Remarkably, on the merits of the dependable and anti-interference PersL lifetimes, this PersL lifetime-based nanoprobe provided highly sensitive and accurate imaging of both endogenous and exogenous hypochlorite. This breakthrough opened up a new way for the development of high-fidelity biosensing in complex matrix systems.
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Affiliation(s)
- Yang Feng
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Xinyi Yang
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Qianli Rao
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Lichun Zhang
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yingying Su
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Yi Lv
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, China
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Kuznetsova V, Coogan Á, Botov D, Gromova Y, Ushakova EV, Gun'ko YK. Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308912. [PMID: 38241607 PMCID: PMC11167410 DOI: 10.1002/adma.202308912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/10/2024] [Indexed: 01/21/2024]
Abstract
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
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Affiliation(s)
- Vera Kuznetsova
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Áine Coogan
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
| | - Dmitry Botov
- Everypixel Media Innovation Group, 021 Fillmore St., PMB 15, San Francisco, CA, 94115, USA
- Neapolis University Pafos, 2 Danais Avenue, Pafos, 8042, Cyprus
| | - Yulia Gromova
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA
| | - Elena V Ushakova
- Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China
| | - Yurii K Gun'ko
- School of Chemistry, CRANN and AMBER Research Centres, Trinity College Dublin, College Green, Dublin, D02 PN40, Ireland
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7
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Dihingia N, Vázquez-Lizardi GA, Wu RJ, Reifsnyder Hickey D. Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning. J Chem Phys 2024; 160:091101. [PMID: 38436439 DOI: 10.1063/5.0188928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 02/09/2024] [Indexed: 03/05/2024] Open
Abstract
For two-dimensional (2D) materials, the exact thickness of the material often dictates its physical and chemical properties. The 2D quantum material WTe2 possesses properties that vary significantly from a single layer to multiple layers, yet it has a complicated crystal structure that makes it difficult to differentiate thicknesses in atomic-resolution images. Furthermore, its air sensitivity and susceptibility to electron beam-induced damage heighten the need for direct ways to determine the thickness and atomic structure without acquiring multiple measurements or transferring samples in ambient atmosphere. Here, we demonstrate a new method to identify the thickness up to ten van der Waals layers in Td-WTe2 using atomic-resolution high-angle annular dark-field scanning transmission electron microscopy image simulation. Our approach is based on analyzing the intensity line profiles of overlapping atomic columns and building a standard neural network model from the line profile features. We observe that it is possible to clearly distinguish between even and odd thicknesses (up to seven layers), without using machine learning, by comparing the deconvoluted peak intensity ratios or the area ratios. The standard neural network model trained on the line profile features allows thicknesses to be distinguished up to ten layers and exhibits an accuracy of up to 94% in the presence of Gaussian and Poisson noise. This method efficiently quantifies thicknesses in Td-WTe2, can be extended to related 2D materials, and provides a pathway to characterize precise atomic structures, including local thickness variations and atomic defects, for few-layer 2D materials with overlapping atomic column positions.
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Affiliation(s)
- Nikalabh Dihingia
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Gabriel A Vázquez-Lizardi
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Ryan J Wu
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Danielle Reifsnyder Hickey
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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8
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [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: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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9
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Li X, Li S, Wu Q. Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting. Molecules 2023; 29:157. [PMID: 38202740 PMCID: PMC10780171 DOI: 10.3390/molecules29010157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/24/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.
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Affiliation(s)
- Xinli Li
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
| | - Suyi Li
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
| | - Qingyi Wu
- Changchun Institute of Optics, Fine Mechanics and Physics, Changchun 130033, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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10
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Zhang J, Li C, Wang H, Yang Z, Hu C, Wu K, Hao J, Liu Z. Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening. Anal Chem 2023; 95:18907-18916. [PMID: 38088810 DOI: 10.1021/acs.analchem.3c05178] [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: 12/27/2023]
Abstract
The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.
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Affiliation(s)
- Jingwei Zhang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Caoling Li
- Equine Science Research and Doping Control Center, Wuhan Business University, Wuhan 430056, China
| | - Han Wang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Zhao Yang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Chengguo Hu
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Kangbing Wu
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Junxing Hao
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Zhihong Liu
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
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11
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Xia X, Sivonxay E, Helms BA, Blau SM, Chan EM. Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization. NANO LETTERS 2023. [PMID: 38038194 DOI: 10.1021/acs.nanolett.3c03568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.
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Affiliation(s)
- Xiaojing Xia
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Eric Sivonxay
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Brett A Helms
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Emory M Chan
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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12
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Ten A, West CA, Jeong S, Hopper ER, Wang Y, Zhu B, Ramasse QM, Ye X, Ringe E. Bimetallic copper palladium nanorods: plasmonic properties and palladium content effects. NANOSCALE ADVANCES 2023; 5:6524-6532. [PMID: 38024297 PMCID: PMC10662198 DOI: 10.1039/d3na00523b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
Cu is an inexpensive alternative plasmonic metal with optical behaviour comparable to Au but with much poorer environmental stability. Alloying with a more stable metal can improve stability and add functionality, with potential effects on the plasmonic properties. Here we investigate the plasmonic behaviour of Cu nanorods and Cu-CuPd nanorods containing up to 46 mass percent Pd. Monochromated scanning transmission electron microscopy electron energy-loss spectroscopy first reveals the strong length dependence of multiple plasmonic modes in Cu nanorods, where the plasmon peaks redshift and narrow with increasing length. Next, we observe an increased damping (and increased linewidth) with increasing Pd content, accompanied by minimal frequency shift. These results are corroborated by and expanded upon with numerical simulations using the electron-driven discrete dipole approximation. This study indicates that adding Pd to nanostructures of Cu is a promising method to expand the scope of their plasmonic applications.
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Affiliation(s)
- Andrey Ten
- Department of Materials Science and Metallurgy, University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK
- Department of Earth Sciences, University of Cambridge Downing Street Cambridge CB2 3EQ UK
| | - Claire A West
- Department of Materials Science and Metallurgy, University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK
- Department of Earth Sciences, University of Cambridge Downing Street Cambridge CB2 3EQ UK
| | - Soojin Jeong
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington Indiana 47405 USA
| | - Elizabeth R Hopper
- Department of Materials Science and Metallurgy, University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK
- Department of Earth Sciences, University of Cambridge Downing Street Cambridge CB2 3EQ UK
- Department of Chemical Engineering and Biotechnology, University of Cambridge Philippa Fawcett Drive Cambridge CB3 0AS UK
| | - Yi Wang
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington Indiana 47405 USA
| | - Baixu Zhu
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington Indiana 47405 USA
| | - Quentin M Ramasse
- School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK
- School of Physics and Astronomy, University of Leeds Leeds LS2 9JS UK
- SuperSTEM, SciTech Daresbury Science and Innovation Campus Keckwick Lane Daresbury WA4 4AD UK
| | - Xingchen Ye
- Department of Chemistry, Indiana University 800 East Kirkwood Avenue Bloomington Indiana 47405 USA
| | - Emilie Ringe
- Department of Materials Science and Metallurgy, University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK
- Department of Earth Sciences, University of Cambridge Downing Street Cambridge CB2 3EQ UK
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Di Filippo D, Sunstrum FN, Khan JU, Welsh AW. Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:9130. [PMID: 38005523 PMCID: PMC10674292 DOI: 10.3390/s23229130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.
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Affiliation(s)
- Daria Di Filippo
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Frédérique N. Sunstrum
- Product Design, School of Design, Faculty of Design, Architecture and Built Environment, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Jawairia U. Khan
- Institute for Biomedical Materials and Devices, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Alec W. Welsh
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- Department of Maternal-Fetal Medicine, Royal Hospital for Women, Randwick, NSW 2031, Australia
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