1
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Lin X, Cheng M, Chen X, Zhang J, Zhao Y, Ai B. Unlocking Predictive Capability and Enhancing Sensing Performances of Plasmonic Hydrogen Sensors via Phase Space Reconstruction and Convolutional Neural Networks. ACS Sens 2024; 9:3877-3888. [PMID: 38741258 DOI: 10.1021/acssensors.3c02651] [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] [Indexed: 05/16/2024]
Abstract
This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithography technique, palladium nanocap arrays are created and their spectral signals are transformed into images using PSR and then trained using CNNs for predicting the hydrogen level. The model achieves accurate predictions with average accuracies of 0.95 for pure hydrogen and 0.97 for mixed gases. Performance improvements observed are a reduction in response time by up to 3.7 times (average 2.1 times) across pressures, SNR increased by up to 9.3 times (average 3.9 times) across pressures, and LOD decreased from 16 Pa to an extrapolated 3 Pa, a 5.3-fold improvement. A practical application of remote hydrogen sensing without electronics in hydrogen environments is actualized and achieves a 0.98 average test accuracy. This methodology reimagines PHS capabilities, facilitating advancements in hydrogen monitoring technologies and intelligent spectrum-based sensing.
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Affiliation(s)
- Xiangxin Lin
- School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing 400044 , P.R. China
| | - Mingyu Cheng
- School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing 400044 , P.R. China
| | - Xinyi Chen
- School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing 400044 , P.R. China
| | - Jinglan Zhang
- School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing 400044 , P.R. China
| | - Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, Georgia 30602 , United States
| | - Bin Ai
- School of Microelectronics and Communication Engineering, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing 400044 , P.R. China
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2
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Chen L, Xia C, Zhao Z, Fu H, Chen Y. AI-Driven Sensing Technology: Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2958. [PMID: 38793814 PMCID: PMC11125233 DOI: 10.3390/s24102958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 04/30/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024]
Abstract
Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.
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Affiliation(s)
| | | | | | - Haoran Fu
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; (L.C.); (C.X.); (Z.Z.)
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3
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Seggio M, Arcadio F, Radicchi E, Cennamo N, Zeni L, Bossi AM. Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor. ACS OMEGA 2024; 9:18984-18994. [PMID: 38708270 PMCID: PMC11064004 DOI: 10.1021/acsomega.3c09485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 05/07/2024]
Abstract
Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.
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Affiliation(s)
- Mimimorena Seggio
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Francesco Arcadio
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Eros Radicchi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Nunzio Cennamo
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Luigi Zeni
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Alessandra Maria Bossi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
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4
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Zhang J, Srivatsa P, Ahmadzai FH, Liu Y, Song X, Karpatne A, Kong ZJ, Johnson BN. Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning. Biosens Bioelectron 2024; 246:115829. [PMID: 38008059 DOI: 10.1016/j.bios.2023.115829] [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: 07/17/2023] [Revised: 10/14/2023] [Accepted: 11/08/2023] [Indexed: 11/28/2023]
Abstract
False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.
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Affiliation(s)
- Junru Zhang
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Purna Srivatsa
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Fazel Haq Ahmadzai
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Yang Liu
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; School of Neuroscience, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Xuerui Song
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Anuj Karpatne
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Zhenyu James Kong
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Blake N Johnson
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; School of Neuroscience, Virginia Tech, Blacksburg, VA, 24061, USA; Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
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5
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Ku W, Lee G, Lee JY, Kim DH, Park KH, Lim J, Cho D, Ha SC, Jung BG, Hwang H, Lee W, Shin H, Jang HS, Lee JO, Hwang JH. Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants. JOURNAL OF HAZARDOUS MATERIALS 2024; 466:133649. [PMID: 38310842 DOI: 10.1016/j.jhazmat.2024.133649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 02/06/2024]
Abstract
Combinations of semiconductor metal oxide (SMO) sensors, electrochemical (EC) sensors, and photoionization detection (PID) sensors were used to discriminate chemical hazards on the basis of machine learning. Sensing data inputs were exploited in the form of either numerical or image data formats, and the classification of chemical hazards with high accuracy was achieved in both cases. Even a small amount of gas sensing or purging data (input for ∼30 s) input can be exploited in machine-learning-based gas discrimination. SMO sensors exhibit high performance even in a single-sensor mode, presumably because of the intrinsic cross-sensitivity of metal oxides, which is otherwise considered a major disadvantage of SMO sensors. EC sensors were enhanced through synergistic integration of sensor combinations with machine learning. For precision detection of multiple target analytes, a minimum number of sensors can be proposed for gas detection/discrimination by combining sensors with dissimilar operating principles. The Type I hybrid sensor combines one SMO sensor, one EC sensor, and one PID sensor and is used to identify NH3 gas mixed with sulfur compounds in simulations of NH3 gas leak accidents in chemical plants. The portable remote sensing module made with a Type I hybrid sensor and LTE module can identify mixed NH3 gas with a detection time of 60 s, demonstrating the potential of the proposed system to quickly respond to hazardous gas leak accidents and prevent additional damage to the environment.
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Affiliation(s)
- Wonseok Ku
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Geonhee Lee
- Thin Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114,South Korea
| | - Ju-Yeon Lee
- Thin Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114,South Korea
| | - Do-Hyeong Kim
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Ki-Hong Park
- Smart City Program, Hongik University, Seoul 04066, Korea
| | - Jongtae Lim
- School of Electronic and Electrical Engineering, Hongik University, Seoul 04066, South Korea
| | - Donghwi Cho
- Thin Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114,South Korea
| | | | | | - Heesu Hwang
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Wooseop Lee
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Huisu Shin
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Ha Seon Jang
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea
| | - Jeong-O Lee
- Thin Materials Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114,South Korea.
| | - Jin-Ha Hwang
- Department of Materials Science and Engineering, Hongik University, Seoul 04066, South Korea.
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6
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Chang IS, Byun SW, Lim TB, Park GM. A Study on E-Nose System in Terms of the Learning Efficiency and Accuracy of Boosting Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:302. [PMID: 38203164 PMCID: PMC10781315 DOI: 10.3390/s24010302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/30/2023] [Accepted: 12/31/2023] [Indexed: 01/12/2024]
Abstract
With the development of the field of e-nose research, the potential for application is increasing in various fields, such as leak measurement, environmental monitoring, and virtual reality. In this study, we characterize electronic nose data as structured data and investigate and analyze the learning efficiency and accuracy of deep learning models that use unstructured data. For this purpose, we use the MOX sensor dataset collected in a wind tunnel, which is one of the most popular public datasets in electronic nose research. Additionally, a gas detection platform was constructed using commercial sensors and embedded boards, and experimental data were collected in a hood environment such as used in chemical experiments. We investigated the accuracy and learning efficiency of deep learning models such as deep learning networks, convolutional neural networks, and long short-term memory, as well as boosting models, which are robust models on structured data, using both public and specially collected datasets. The results showed that the boosting models had a faster and more robust performance than deep learning series models.
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Affiliation(s)
- Il-Sik Chang
- The Graduate School of Nano IT Design Fusion, Seoul National University of S&T, Seoul 01811, Republic of Korea;
| | - Sung-Woo Byun
- Digital Innovation Support Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea;
| | - Tae-Beom Lim
- Intelligent Information Research Division, Korea Electronics Technology Institute, Seongnam 13488, Republic of Korea;
| | - Goo-Man Park
- Department of Smart ICT Convergence Engineering, Seoul National University of S&T, Seoul 01811, Republic of Korea
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7
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Minho LAC, Cardeal ZDL, Menezes HC. A deep learning-based simulator for comprehensive two-dimensional GC applications. J Sep Sci 2023; 46:e2300187. [PMID: 37525343 DOI: 10.1002/jssc.202300187] [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: 03/22/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/02/2023]
Abstract
Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R2 = 0.9202) and 0.014 (relative average deviation = 1.67%, R2 = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.
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Affiliation(s)
- Lucas Almir Cavalcante Minho
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
| | - Zenilda de Lourdes Cardeal
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
| | - Helvécio Costa Menezes
- Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil
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8
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9
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Min J, Tu J, Xu C, Lukas H, Shin S, Yang Y, Solomon SA, Mukasa D, Gao W. Skin-Interfaced Wearable Sweat Sensors for Precision Medicine. Chem Rev 2023; 123:5049-5138. [PMID: 36971504 PMCID: PMC10406569 DOI: 10.1021/acs.chemrev.2c00823] [Citation(s) in RCA: 82] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.
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Affiliation(s)
- Jihong Min
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Jiaobing Tu
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Changhao Xu
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Heather Lukas
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Soyoung Shin
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Yiran Yang
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Samuel A. Solomon
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Daniel Mukasa
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
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10
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Giordano GF, Ferreira LF, Bezerra ÍRS, Barbosa JA, Costa JNY, Pimentel GJC, Lima RS. Machine learning toward high-performance electrochemical sensors. Anal Bioanal Chem 2023:10.1007/s00216-023-04514-z. [PMID: 36637495 PMCID: PMC9838410 DOI: 10.1007/s00216-023-04514-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/14/2023]
Abstract
The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.
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Affiliation(s)
- Gabriela F. Giordano
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil
| | - Larissa F. Ferreira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Júlia A. Barbosa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
| | - Juliana N. Y. Costa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil
| | - Gabriel J. C. Pimentel
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,School of Sciences, São Paulo State University, Bauru, São Paulo 17033-360 Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-100 Brazil ,Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970 Brazil ,Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580 Brazil ,São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil
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11
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Davies JC, Pattison D, Hirst JD. Machine learning for yield prediction for chemical reactions using in situ sensors. J Mol Graph Model 2023; 118:108356. [PMID: 36272195 DOI: 10.1016/j.jmgm.2022.108356] [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: 08/11/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 11/28/2022]
Abstract
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including temperature, pressure, and colour. Colour was a good predictor of product formation for this reaction and machine learning models were able to learn which of the properties were important. Predictions for the current product formation (in terms of % yield) had a mean absolute error of 1.2%. For predicting 30, 60 and 120 min ahead the error rose to 3.4, 4.1 and 4.6%, respectively. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.
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Affiliation(s)
- Joseph C Davies
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | | | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
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12
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Yang GG, Ko J, Choi HJ, Kim DH, Han KH, Kim JH, Kim MH, Park C, Jin HM, Kim ID, Kim SO. Multilevel Self-Assembly of Block Copolymers and Polymer Colloids for a Transparent and Sensitive Gas Sensor Platform. ACS NANO 2022; 16:18767-18776. [PMID: 36374261 DOI: 10.1021/acsnano.2c07499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The recent emerging significance of the Internet of Things (IoT) demands sensor devices to be integrated with many different functional structures and devices while conserving their original functionalities. To this end, optical transparency and mechanical flexibility of sensor devices are critical requirements for optimal integration as well as high sensitivity. In this work, a transparent, flexible, and sensitive gas sensor building platform is introduced by using multilevel self-assembly of block copolymers (BCPs) and polystyrene (PS) colloids. For the demonstration of an H2 gas sensor, a hierarchically porous Pd metal mesh structure is obtained by overlaying the two different patterned template structures with synergistic, distinctive characteristic length scales. The hierarchical Pd mesh shows not only high transparency over 90% but also superior sensing performance in terms of response and recovery time owing to enhanced Pd-to-hydride ratio and short H2 diffusion lengths from the enlarged active surface areas. The hierarchical morphology also endows high mechanical flexibility while securing reliable sensing performance even under severe mechanical deformation cycles. Our scalable self-assembly based multiscale nanopatterning offers an intriguing generalized platform for many different multifunctional devices requiring hidden in situ monitoring of environmental signals.
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Affiliation(s)
- Geon Gug Yang
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | | | - Hee Jae Choi
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | | | - Kyu Hyo Han
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jang Hwan Kim
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Min Hyuk Kim
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | | | - Hyeon Min Jin
- Department of Organic Materials Engineering, Chungnam National University, Daejeon 34134, Korea
| | | | - Sang Ouk Kim
- National Creative Research Initiative Center for Multi-Dimensional Directed Nanoscale Assembly, Department of Materials Science and Engineering, Korea Advance Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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13
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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Noreldeen HAA, Huang KY, Wu GW, Peng HP, Deng HH, Chen W. Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B 6 Derivatives. Anal Chem 2022; 94:9287-9296. [PMID: 35723526 DOI: 10.1021/acs.analchem.2c00655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Vitamin B6 derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 μM). The determination coefficient (R2) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry.
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Affiliation(s)
- Hamada A A Noreldeen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China.,National Institute of Oceanography and Fisheries, NIOF, Cairo 4262110, Egypt
| | - Kai-Yuan Huang
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Gang-Wei Wu
- Department of Pharmacy, Fujian Provincial Hospital, Fuzhou 350001, China
| | - Hua-Ping Peng
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Hao-Hua Deng
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
| | - Wei Chen
- Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350004, China
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15
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Affiliation(s)
- Hee-Tae Jung
- Korea Advanced Institute of Science Technology, Daejeon 34141, Republic of Korea
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16
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Yano J, Gaffney KJ, Gregoire J, Hung L, Ourmazd A, Schrier J, Sethian JA, Toma FM. The case for data science in experimental chemistry: examples and recommendations. Nat Rev Chem 2022; 6:357-370. [PMID: 37117931 DOI: 10.1038/s41570-022-00382-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 12/31/2022]
Abstract
The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.
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17
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18
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Puthongkham P, Wirojsaengthong S, Suea-Ngam A. Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry. Analyst 2021; 146:6351-6364. [PMID: 34585185 DOI: 10.1039/d1an01148k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.
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Affiliation(s)
- Pumidech Puthongkham
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. .,Electrochemistry and Optical Spectroscopy Center of Excellence (EOSCE), Chulalongkorn University, Bangkok 10330, Thailand.,Center of Excellence in Responsive Wearable Materials, Chulalongkorn University, Bangkok 10330, Thailand
| | - Supacha Wirojsaengthong
- Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
| | - Akkapol Suea-Ngam
- Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK
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19
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Ma YP, Li Q, Luo JB, Huang CZ, Zhou J. Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning. Anal Chem 2021; 93:12131-12138. [PMID: 34432436 DOI: 10.1021/acs.analchem.1c02813] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.
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Affiliation(s)
- Yun Peng Ma
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Qian Li
- Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Jun Bo Luo
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Jun Zhou
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China.,Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
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21
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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22
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Advanced surface analysis using GCIB-C60++-tandem-ToF-SIMS and GCIB-XPS of 2-mercaptobenzimidazole corrosion inhibitor on brass. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105495] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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