Pub Date : 2023-06-01Epub Date: 2023-04-28DOI: 10.1149/2754-2726/accd7e
Thomas Young, Vincent Clark, Netzahualcóyotl Arroyo-Currás, Jason Heikenfeld
Real-time continuous monitoring of proteins in-vivo holds great potential for personalized medical applications. Unfortunately, a prominent knowledge gap exists in the fundamental biology regarding protein transfer and correlation between interstitial fluid and blood. Additionally, technological sensing will require affinity-based platforms that cannot be robustly protected in-vivo and will therefore be challenged in sensitivity, longevity, and fouling over multi-day to week timelines. Here we use electrochemical aptamer sensors as a model system to discuss further research necessary to achieve continuous protein sensing.
{"title":"Perspective-The Feasibility of Continuous Protein Monitoring in Interstitial Fluid.","authors":"Thomas Young, Vincent Clark, Netzahualcóyotl Arroyo-Currás, Jason Heikenfeld","doi":"10.1149/2754-2726/accd7e","DOIUrl":"10.1149/2754-2726/accd7e","url":null,"abstract":"<p><p>Real-time continuous monitoring of proteins in-vivo holds great potential for personalized medical applications. Unfortunately, a prominent knowledge gap exists in the fundamental biology regarding protein transfer and correlation between interstitial fluid and blood. Additionally, technological sensing will require affinity-based platforms that cannot be robustly protected in-vivo and will therefore be challenged in sensitivity, longevity, and fouling over multi-day to week timelines. Here we use electrochemical aptamer sensors as a model system to discuss further research necessary to achieve continuous protein sensing.</p>","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9456291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.1149/2754-2726/acd0c0
R. Elaswad, N. El-Sheimy, A. A. Mohamad
The article reviews most published inertial sensor technologies, including dynamically tuned, optical, MEMS vibratory, mechanical, solid-state, and fluid-based. The working principles of the technologies are elaborated. Also, the advantages and disadvantages of those sensors are laid out. Owing to its excellent overall performance, such as its simple structure, low cost, large measurement range, etc, the current review focuses on the state-of-the-art of fluid-based technology of accelerometers and gyroscopes. The sensing elements of the fluid-based technology that are used in the accelerometer and gyroscope are explained. Moreover, a comparison and analysis of those sensing elements are presented. The comparison shows that the thermal resistor has five orders of magnitude which is the highest dynamic range. However, the porous transducer is higher in bandwidth which is almost 120 Hz. Furthermore, the particle imaging velocimetry gyroscope (PIVG) is reviewed. The PIVG is an innovative technology that is used to measure the angular rate where fluid is used as proof of mass. The review shows that the PIVG is low-cost and almost drift-free. Additionally, compared to commercially available gyroscopes, PIVG provides a superior signal-to-noise ratio (SNR).
{"title":"Review—Basic and Advanced Inertial Navigation Fluid-Based Technology","authors":"R. Elaswad, N. El-Sheimy, A. A. Mohamad","doi":"10.1149/2754-2726/acd0c0","DOIUrl":"https://doi.org/10.1149/2754-2726/acd0c0","url":null,"abstract":"The article reviews most published inertial sensor technologies, including dynamically tuned, optical, MEMS vibratory, mechanical, solid-state, and fluid-based. The working principles of the technologies are elaborated. Also, the advantages and disadvantages of those sensors are laid out. Owing to its excellent overall performance, such as its simple structure, low cost, large measurement range, etc, the current review focuses on the state-of-the-art of fluid-based technology of accelerometers and gyroscopes. The sensing elements of the fluid-based technology that are used in the accelerometer and gyroscope are explained. Moreover, a comparison and analysis of those sensing elements are presented. The comparison shows that the thermal resistor has five orders of magnitude which is the highest dynamic range. However, the porous transducer is higher in bandwidth which is almost 120 Hz. Furthermore, the particle imaging velocimetry gyroscope (PIVG) is reviewed. The PIVG is an innovative technology that is used to measure the angular rate where fluid is used as proof of mass. The review shows that the PIVG is low-cost and almost drift-free. Additionally, compared to commercially available gyroscopes, PIVG provides a superior signal-to-noise ratio (SNR).","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44581462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-24DOI: 10.1149/2754-2726/acc74d
Hyusim Park, Liem H. T. Nguyen, Shanthala Lakshminarayana, Yuze Sun, Sungyong Jung
Wearable devices can be found as numerous types of health devices ranging from monitoring hazardous environments to detecting physiological signals. Its utilities have also shown encouraging improvements toward personalized healthcare. Thus, many kinds of wearable health devices (WHD) have been reported and developed. Those reports highlight their efficiency in monitoring harmful environmental factors and various diseases for diagnostic and treatment purposes. This paper proposes a watch-type of dual-mode WHD that can perform gaseous phase detection to monitor dangerous environmental situations such as bad air quality and aqueous phase detection to measure physiological signals from human sweat or blood. The proposed system was fabricated on a printed circuit board and its size is 3 cm × 3 cm which is suitable as a wearable device. It also consumes very low power and has capabilities of wired/wireless charging and wired/wireless communication. Furthermore, customized windows and Android applications have been developed to visualize obtained data in a user’s computer or smartphone. The system was tested both electrically and chemically and showed promising results as the WHD.
{"title":"Watch-Type Dual-Mode Wearable Health Device","authors":"Hyusim Park, Liem H. T. Nguyen, Shanthala Lakshminarayana, Yuze Sun, Sungyong Jung","doi":"10.1149/2754-2726/acc74d","DOIUrl":"https://doi.org/10.1149/2754-2726/acc74d","url":null,"abstract":"Wearable devices can be found as numerous types of health devices ranging from monitoring hazardous environments to detecting physiological signals. Its utilities have also shown encouraging improvements toward personalized healthcare. Thus, many kinds of wearable health devices (WHD) have been reported and developed. Those reports highlight their efficiency in monitoring harmful environmental factors and various diseases for diagnostic and treatment purposes. This paper proposes a watch-type of dual-mode WHD that can perform gaseous phase detection to monitor dangerous environmental situations such as bad air quality and aqueous phase detection to measure physiological signals from human sweat or blood. The proposed system was fabricated on a printed circuit board and its size is 3 cm × 3 cm which is suitable as a wearable device. It also consumes very low power and has capabilities of wired/wireless charging and wired/wireless communication. Furthermore, customized windows and Android applications have been developed to visualize obtained data in a user’s computer or smartphone. The system was tested both electrically and chemically and showed promising results as the WHD.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46879189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-13DOI: 10.1149/2754-2726/acc3ab
Shakti Singh, A. Dzeranov, L. Bondarenko, K. Kydralieva, G. Dzhardimalieva, A. Babaytsev, G. Kugabaeva, N. Golubeva, B. Yadav
The present work focuses on the synthesis of Fe3O4 magnetite core@shell type nanoparticles modified with three types of ligands: Magnetite with activated carbon (MAC), Magnetite with silica (tetraethoxysilane, TEOS, and 3-aminopropyltriethoxysilane, APTES) (MTA) and Magnetite with silica, APTES and humic acids (MTAH). The MTAH sample shows greater porosity in comparison to MTA, and MAC samples. The band gap of MTAH is 4.08 eV, which is higher than MTA (2.92 eV), and MAC (2.80 eV). Rietveld quantitative phase analysis of all derivatives was performed and compared with all three samples. The LPG sensing at room temperature shows the highest sensor response of 9.42, in comparison to 3.87 sensor response for MAC, and 4.60 for MTA. This approximately double sensor response increment is justified with the help of band gap, porosity, and size of all 3 the samples. The MTAH sample shows the lowest response-recovery time of 9.33 and 10.78 s respectively in comparison to MAC and MTA samples. In conclusion, this manuscript describes the synthesis procedure of different derivatives of Fe3O4 core@shell materials along with the relation of LPG sensing with different parameters of the materials.
{"title":"Modified Fe3O4 Magnetite Core@Shell Type Nanomaterials for Highly-Responsive LPG Sensing: A Comparative Analysis","authors":"Shakti Singh, A. Dzeranov, L. Bondarenko, K. Kydralieva, G. Dzhardimalieva, A. Babaytsev, G. Kugabaeva, N. Golubeva, B. Yadav","doi":"10.1149/2754-2726/acc3ab","DOIUrl":"https://doi.org/10.1149/2754-2726/acc3ab","url":null,"abstract":"The present work focuses on the synthesis of Fe3O4 magnetite core@shell type nanoparticles modified with three types of ligands: Magnetite with activated carbon (MAC), Magnetite with silica (tetraethoxysilane, TEOS, and 3-aminopropyltriethoxysilane, APTES) (MTA) and Magnetite with silica, APTES and humic acids (MTAH). The MTAH sample shows greater porosity in comparison to MTA, and MAC samples. The band gap of MTAH is 4.08 eV, which is higher than MTA (2.92 eV), and MAC (2.80 eV). Rietveld quantitative phase analysis of all derivatives was performed and compared with all three samples. The LPG sensing at room temperature shows the highest sensor response of 9.42, in comparison to 3.87 sensor response for MAC, and 4.60 for MTA. This approximately double sensor response increment is justified with the help of band gap, porosity, and size of all 3 the samples. The MTAH sample shows the lowest response-recovery time of 9.33 and 10.78 s respectively in comparison to MAC and MTA samples. In conclusion, this manuscript describes the synthesis procedure of different derivatives of Fe3O4 core@shell materials along with the relation of LPG sensing with different parameters of the materials.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49090336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-06DOI: 10.1149/2754-2726/acc190
Vibhas Chugh, Adreeja Basu, A. Kaushik, A. Basu
The 5th/6th generation bio-sensing technology is an emerging field which connects smart technologies like Artificial Intelligence, Internet of Things and Machine Learning with efficient micro/nano-enabled sensing platform for making point-of-care (POC) devices to investigate health management strategies. Recently, the integration and interfacing between quantum measurement, signaling, and optimized bio-actives has led to investigate the minute biological events with anomalous sensitivity. Such technologies are expected to provide the possibility to measure and record changes at quantum scales with varying pressure, temperature, and electromagnetic fields. Considering current scenarios, this perspective critically highlights state-of-art quantum sensing technology along with their challenges and prospects.
{"title":"Progression in Quantum Sensing/Bio-Sensing Technologies for Healthcare","authors":"Vibhas Chugh, Adreeja Basu, A. Kaushik, A. Basu","doi":"10.1149/2754-2726/acc190","DOIUrl":"https://doi.org/10.1149/2754-2726/acc190","url":null,"abstract":"The 5th/6th generation bio-sensing technology is an emerging field which connects smart technologies like Artificial Intelligence, Internet of Things and Machine Learning with efficient micro/nano-enabled sensing platform for making point-of-care (POC) devices to investigate health management strategies. Recently, the integration and interfacing between quantum measurement, signaling, and optimized bio-actives has led to investigate the minute biological events with anomalous sensitivity. Such technologies are expected to provide the possibility to measure and record changes at quantum scales with varying pressure, temperature, and electromagnetic fields. Considering current scenarios, this perspective critically highlights state-of-art quantum sensing technology along with their challenges and prospects.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42817259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01Epub Date: 2023-03-29DOI: 10.1149/2754-2726/acc4d9
Alexander Shaver, Netzahualcóyotl Arroyo-Currás
Electrochemical biosensors are a powerful and rapidly evolving molecular monitoring technology. Evidenced by the success of the continuous glucose monitor in managing Type 1 Diabetes, these sensors are capable of precise, accurate measurements in unprocessed biological environments. Nucleic acid-based electrochemical sensors (NBEs) are a specific type of biosensor that employs the target binding and conformational dynamics of nucleic acids for signal transduction. Currently, the vast majority of NBEs are fabricated via self-assembly of alkylthiols on Au electrodes. However, this architecture is limited in scope, as Au electrodes are not universally deployable for all potential NBE applications. Here, to expand the repertoire of materials on which NBEs can be made, we describe the multistep procedure for creating sensing monolayers of alkylphosphonic acids on a conductive oxide surface. Using such monolayers on indium tin oxide (ITO)-coated glass slides, we couple redox reporter-modified nucleic acids and demonstrate signaling of procaine-binding NBE sensors in buffer and human serum. We investigate the operational stability of these NBE sensors to reveal faster signal loss relative to benchmark thiol-on-gold sensing layers, a result that arises due to poor stability of the underlying ITO. Finally, we discuss future directions to continue expansion of NBE sensor materials and applications.
{"title":"Expanding the Monolayer Scope for Nucleic Acid-Based Electrochemical Sensors Beyond Thiols on Gold: Alkylphosphonic Acids on ITO.","authors":"Alexander Shaver, Netzahualcóyotl Arroyo-Currás","doi":"10.1149/2754-2726/acc4d9","DOIUrl":"10.1149/2754-2726/acc4d9","url":null,"abstract":"<p><p>Electrochemical biosensors are a powerful and rapidly evolving molecular monitoring technology. Evidenced by the success of the continuous glucose monitor in managing Type 1 Diabetes, these sensors are capable of precise, accurate measurements in unprocessed biological environments. Nucleic acid-based electrochemical sensors (NBEs) are a specific type of biosensor that employs the target binding and conformational dynamics of nucleic acids for signal transduction. Currently, the vast majority of NBEs are fabricated via self-assembly of alkylthiols on Au electrodes. However, this architecture is limited in scope, as Au electrodes are not universally deployable for all potential NBE applications. Here, to expand the repertoire of materials on which NBEs can be made, we describe the multistep procedure for creating sensing monolayers of alkylphosphonic acids on a conductive oxide surface. Using such monolayers on indium tin oxide (ITO)-coated glass slides, we couple redox reporter-modified nucleic acids and demonstrate signaling of procaine-binding NBE sensors in buffer and human serum. We investigate the operational stability of these NBE sensors to reveal faster signal loss relative to benchmark thiol-on-gold sensing layers, a result that arises due to poor stability of the underlying ITO. Finally, we discuss future directions to continue expansion of NBE sensor materials and applications.</p>","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10348772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1149/2754-2726/acbe0c
Neal Ma, Sleight Halley, K. Ramaiyan, F. Garzon, L. Tsui
Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La0.87Sr0.13CrO3, Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH4, two types of natural gas simulants, and CH4+NH3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.
{"title":"Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays","authors":"Neal Ma, Sleight Halley, K. Ramaiyan, F. Garzon, L. Tsui","doi":"10.1149/2754-2726/acbe0c","DOIUrl":"https://doi.org/10.1149/2754-2726/acbe0c","url":null,"abstract":"Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La0.87Sr0.13CrO3, Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH4, two types of natural gas simulants, and CH4+NH3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49121417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-13DOI: 10.1149/2754-2726/acb2da
Prateek Mehta, Srikanth Namuduri, L. Barbé, Stephanie Lam, Zohreh Faghihmonzavi, Vivek Kamat, S. Finkbeiner, S. Bhansali
Comet assay is a widely used technique to assess and quantify DNA damage in individual cells. Recently, researchers have applied various deep learning techniques to automate the analysis of comet assay. Image analysis using deep learning allows combining multiple parameters of images and performing computation at a pixel level to provide quantifiable information about the comets. The current deep learning analysis algorithms use a single neural network as a standard method, which relies on many comet images and prone to high variance in predictions. Here, we propose a new ensemble model consisting of a collection of deep learning networks with different configurations and different initial random weights trained on the same dataset to calculate one weighted prediction for DNA damage quantification. To develop this model, we curated a trainable comet assay image dataset consisting of1309 images with 9204 extracted features of cell head and tail length, area, etc With the proposed method we could achieve significantly higher accuracy (R2 = 89.3%, compared to 74% with the standard single neural network as reported in data published by M. D. Zeiler and R Fergus (European conference on computer vision, pp. 818–833 2014). Furthermore, deep regression with the proposed architecture produced much more reliable and accurate results than conventional method.
{"title":"AI Enabled Ensemble Deep Learning Method for Automated Sensing and Quantification of DNA Damage in Comet Assay","authors":"Prateek Mehta, Srikanth Namuduri, L. Barbé, Stephanie Lam, Zohreh Faghihmonzavi, Vivek Kamat, S. Finkbeiner, S. Bhansali","doi":"10.1149/2754-2726/acb2da","DOIUrl":"https://doi.org/10.1149/2754-2726/acb2da","url":null,"abstract":"Comet assay is a widely used technique to assess and quantify DNA damage in individual cells. Recently, researchers have applied various deep learning techniques to automate the analysis of comet assay. Image analysis using deep learning allows combining multiple parameters of images and performing computation at a pixel level to provide quantifiable information about the comets. The current deep learning analysis algorithms use a single neural network as a standard method, which relies on many comet images and prone to high variance in predictions. Here, we propose a new ensemble model consisting of a collection of deep learning networks with different configurations and different initial random weights trained on the same dataset to calculate one weighted prediction for DNA damage quantification. To develop this model, we curated a trainable comet assay image dataset consisting of1309 images with 9204 extracted features of cell head and tail length, area, etc With the proposed method we could achieve significantly higher accuracy (R2 = 89.3%, compared to 74% with the standard single neural network as reported in data published by M. D. Zeiler and R Fergus (European conference on computer vision, pp. 818–833 2014). Furthermore, deep regression with the proposed architecture produced much more reliable and accurate results than conventional method.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48258673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1149/2754-2726/acb27a
C. Manjunatha, K. Shwetha, Y. Athreya, S. G. Kumar, M. K. S. Kamath
Currently and also in future, the flexible and wearable strain sensor would be in high demand due to its direct applications in biomedical health monitoring and other engineering applications. The challenge is to make the flexible and wearable sensor to function continuously with no additional wired powered supply system. In line with this, there has been considerable research works towards the integration of supercapacitor into flexible and wearable strain sensors, to make them self-powered and more efficient. This perspective provides more insights on design and fabrication of flexible and wearable strain sensor, working, components, and materials used, integration with supercapacitor, challenges and future scope.
{"title":"Perspective—Supercapacitor-Powered Flexible Wearable Strain Sensors","authors":"C. Manjunatha, K. Shwetha, Y. Athreya, S. G. Kumar, M. K. S. Kamath","doi":"10.1149/2754-2726/acb27a","DOIUrl":"https://doi.org/10.1149/2754-2726/acb27a","url":null,"abstract":"Currently and also in future, the flexible and wearable strain sensor would be in high demand due to its direct applications in biomedical health monitoring and other engineering applications. The challenge is to make the flexible and wearable sensor to function continuously with no additional wired powered supply system. In line with this, there has been considerable research works towards the integration of supercapacitor into flexible and wearable strain sensors, to make them self-powered and more efficient. This perspective provides more insights on design and fabrication of flexible and wearable strain sensor, working, components, and materials used, integration with supercapacitor, challenges and future scope.","PeriodicalId":72870,"journal":{"name":"ECS sensors plus","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43513446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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