Pub Date : 2021-09-16DOI: 10.3389/fsens.2021.731928
Kai-Cheng Yan, Axel Steinbrueck, A. Sedgwick, T. James
Over the past 30 years fluorescent chemosensors have evolved to incorporate many optical-based modalities and strategies. In this perspective we seek to highlight the current state of the art as well as provide our viewpoint on the most significant future challenges remaining in the area. To underscore current trends in the field and to facilitate understanding of the area, we provide the reader with appropriate contemporary examples. We then conclude with our thoughts on the most probable directions that chemosensor development will take in the not-too-distant future.
{"title":"Fluorescent Chemosensors for Ion and Molecule Recognition: The Next Chapter","authors":"Kai-Cheng Yan, Axel Steinbrueck, A. Sedgwick, T. James","doi":"10.3389/fsens.2021.731928","DOIUrl":"https://doi.org/10.3389/fsens.2021.731928","url":null,"abstract":"Over the past 30 years fluorescent chemosensors have evolved to incorporate many optical-based modalities and strategies. In this perspective we seek to highlight the current state of the art as well as provide our viewpoint on the most significant future challenges remaining in the area. To underscore current trends in the field and to facilitate understanding of the area, we provide the reader with appropriate contemporary examples. We then conclude with our thoughts on the most probable directions that chemosensor development will take in the not-too-distant future.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43595473","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}
A nanoparticle-based few-mode multi-core fiber (FM-MCF) localized surface plasmon resonance (LSPR) biosensor is proposed and analyzed using the finite element method (FEM). It’s critical to narrow the loss spectrum and improve the coupling efficiency, which makes it have high resolution and high sensitivity. With the aid of open air holes, the gold nanoparticles are easily assembled on the surface of this FM-MCF LSPR biosensor. Through multiple investigations, the performance of the sensor can be improved by properly setting gold nanoparticle configurations, such as radius, positions, shapes, and nanoparticle arrays. The simulation results show that when three circular gold nanoparticles with a radius of 150 nm are placed symmetrically in the open air hole and the angle between adjacent nanoparticles is 5°, the maximum sensitivity of 7,351.6 nm/RIU (LP02y mode na = 1.38) can be obtained in the sensing range of 1.33–1.38, which covers the refractive index (RI) of biological fluids, such as bovine serum albumin (BSA) solution and human Immunoglobulin G.
{"title":"Nanoparticle-Based FM-MCF LSPR Biosensor With Open Air-Hole","authors":"Chuanhao Yang, Shiyan Xiao, Qi Wang, Hongxia Zhang, Hui Yu, Dagong Jia","doi":"10.3389/fsens.2021.751952","DOIUrl":"https://doi.org/10.3389/fsens.2021.751952","url":null,"abstract":"A nanoparticle-based few-mode multi-core fiber (FM-MCF) localized surface plasmon resonance (LSPR) biosensor is proposed and analyzed using the finite element method (FEM). It’s critical to narrow the loss spectrum and improve the coupling efficiency, which makes it have high resolution and high sensitivity. With the aid of open air holes, the gold nanoparticles are easily assembled on the surface of this FM-MCF LSPR biosensor. Through multiple investigations, the performance of the sensor can be improved by properly setting gold nanoparticle configurations, such as radius, positions, shapes, and nanoparticle arrays. The simulation results show that when three circular gold nanoparticles with a radius of 150 nm are placed symmetrically in the open air hole and the angle between adjacent nanoparticles is 5°, the maximum sensitivity of 7,351.6 nm/RIU (LP02y mode na = 1.38) can be obtained in the sensing range of 1.33–1.38, which covers the refractive index (RI) of biological fluids, such as bovine serum albumin (BSA) solution and human Immunoglobulin G.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42674076","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 : 2021-09-08DOI: 10.3389/fsens.2021.679908
F. Lenartz, Marie Dury, B. Bergmans, V. Hutsemekers, V. Broun, Christophe Brose, S. Guichaux
The increasing availability of low-cost sensors and open source projects make it easier than ever for a maker to build his own air quality node. Nonetheless, depending on one’s goal and its related data quality objective, to customize an existing project or to build a specific printed circuit board may still be very useful. In the framework of the Outdoor and Indoor Exposure project, a portable mini-station has been developed, tested and then used in two experiments: exposure assessment and complementary network measurement. The present paper focuses on the description of the equipment that was designed and prototyped, as well as on the tests that were made in the lab and in the field to evaluate its overall performance and that of its different sensors. Finally, we present what we consider to be its main drawbacks and our perspectives for further development and tests.
{"title":"Antilope, A Portable Low-Cost Sensor System for the Assessment of Indoor and Outdoor Air Pollution Exposure","authors":"F. Lenartz, Marie Dury, B. Bergmans, V. Hutsemekers, V. Broun, Christophe Brose, S. Guichaux","doi":"10.3389/fsens.2021.679908","DOIUrl":"https://doi.org/10.3389/fsens.2021.679908","url":null,"abstract":"The increasing availability of low-cost sensors and open source projects make it easier than ever for a maker to build his own air quality node. Nonetheless, depending on one’s goal and its related data quality objective, to customize an existing project or to build a specific printed circuit board may still be very useful. In the framework of the Outdoor and Indoor Exposure project, a portable mini-station has been developed, tested and then used in two experiments: exposure assessment and complementary network measurement. The present paper focuses on the description of the equipment that was designed and prototyped, as well as on the tests that were made in the lab and in the field to evaluate its overall performance and that of its different sensors. Finally, we present what we consider to be its main drawbacks and our perspectives for further development and tests.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42245406","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 : 2021-09-02DOI: 10.3389/fsens.2021.711592
Margaret McCaul, P. Magni, S. Jordan, E. McNamara, A. Satta, D. Diamond, A. Ribotti
A portable sensing platform for the detection of nutrients (PO4 3−, NO2 −, NO3 −) in natural waters has been realized through the use of rapid prototyping techniques, colorimetric chemistries, electronics, and LED-based optical detection. The sensing platform is modular in design incorporating interchangeable optical detection units, with a component cost per unit of ca. €300, and small form factor (20 cm × 6 cm x 3.5 cm). Laboratory testing and validation of the platform was performed prior to deployment at the CNR Dirigibile Italia Arctic Research Station, Ny-Aselund (79°N, 12°E). Results obtained showed excellent linear response, with a limit of detection of 0.05 μM (NO2 −, NO3 −), and 0.03 μM (PO4 3−). On the June 22, 2016 a field campaign took place within Kongsfjorden, Ny-Aselund (78.5–79°N, 11.6–12.6°E), during which 55 water samples were acquired using 10 L Niskin bottles on board the MS Teisten research vessel. 23 hydrological casts were also performed using a Seabird 19plus V2 SeaCAT Profiler CTD probe with turbidity and dissolved oxygen sensors. Water samples were subsequently analyzed for PO4 3−, NO2 −, NO3 − at the CNR Dirigibile Italia Arctic Research Station Laboratory using the adaptive sensing platform. Nutrient concentrations were compared to hydrological data to assess the processes that influence the nutrient concentrations within the Fjord. This research highlights the potential use of the adaptive sensing platform in remote locations as a stand-alone platform and/or for the validation of deployable environmental sensor networks.
{"title":"Nutrient Analysis in Arctic Waters Using a Portable Sensing Platform","authors":"Margaret McCaul, P. Magni, S. Jordan, E. McNamara, A. Satta, D. Diamond, A. Ribotti","doi":"10.3389/fsens.2021.711592","DOIUrl":"https://doi.org/10.3389/fsens.2021.711592","url":null,"abstract":"A portable sensing platform for the detection of nutrients (PO4 3−, NO2 −, NO3 −) in natural waters has been realized through the use of rapid prototyping techniques, colorimetric chemistries, electronics, and LED-based optical detection. The sensing platform is modular in design incorporating interchangeable optical detection units, with a component cost per unit of ca. €300, and small form factor (20 cm × 6 cm x 3.5 cm). Laboratory testing and validation of the platform was performed prior to deployment at the CNR Dirigibile Italia Arctic Research Station, Ny-Aselund (79°N, 12°E). Results obtained showed excellent linear response, with a limit of detection of 0.05 μM (NO2 −, NO3 −), and 0.03 μM (PO4 3−). On the June 22, 2016 a field campaign took place within Kongsfjorden, Ny-Aselund (78.5–79°N, 11.6–12.6°E), during which 55 water samples were acquired using 10 L Niskin bottles on board the MS Teisten research vessel. 23 hydrological casts were also performed using a Seabird 19plus V2 SeaCAT Profiler CTD probe with turbidity and dissolved oxygen sensors. Water samples were subsequently analyzed for PO4 3−, NO2 −, NO3 − at the CNR Dirigibile Italia Arctic Research Station Laboratory using the adaptive sensing platform. Nutrient concentrations were compared to hydrological data to assess the processes that influence the nutrient concentrations within the Fjord. This research highlights the potential use of the adaptive sensing platform in remote locations as a stand-alone platform and/or for the validation of deployable environmental sensor networks.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49033025","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 : 2021-08-10DOI: 10.3389/fsens.2021.719161
S. Sriprasertsuk, Shuai Zhang, G. Wallace, Jun Chen, J. Varcoe, C. Crean
A modified carbon fibre yarn sensor was developed for the voltammetric determination of paracetamol and its interferents (dopamine and ascorbic acid). Reduced graphene oxide (rGO) was electrochemically deposited onto a carbon fibre yarn. Further modification was achieved using polypyrrole (PPy) coated onto the rGO carbon fibre yarn via electropolymerisation of pyrrole with cyclic voltammetry (CV). The surface of the rGO and PPy-rGO carbon fibre electrodes were characterised using Raman spectroscopy and scanning electron microscopy. The rGO and PPy-rGO carbon fibres had a 3.5-fold and 7-fold larger electrochemical surface area compared to bare carbon fibre (calculated using the Randles-Sevcik equation). Two clearly distinguished oxidation peaks at 0.49 and 0.25 V (vs. Ag/AgCl) were observed at the rGO fibre electrode during the simultaneous detection of paracetamol and dopamine, respectively, by CV. The detection limit (3σ S/N) of the rGO carbon fibre electrode for differential pulse voltammetry (DPV) determination of paracetamol was at 21.1 and 6.0 µM for dopamine. In comparison, the simultaneous determination of paracetamol and dopamine by CV at the PPy-rGO fibre electrode gave oxidation peaks of paracetamol and dopamine at 0.55 and 0.25 V (vs. Ag/AgCl), respectively. The detection limit (3σ S/N) for paracetamol was notably improved to 3.7 µM and maintained at 6.0 µM for dopamine at the PPy-rGO carbon fibre electrode during DPV.
{"title":"Reduced Graphene Oxide Carbon Yarn Electrodes for Drug Sensing","authors":"S. Sriprasertsuk, Shuai Zhang, G. Wallace, Jun Chen, J. Varcoe, C. Crean","doi":"10.3389/fsens.2021.719161","DOIUrl":"https://doi.org/10.3389/fsens.2021.719161","url":null,"abstract":"A modified carbon fibre yarn sensor was developed for the voltammetric determination of paracetamol and its interferents (dopamine and ascorbic acid). Reduced graphene oxide (rGO) was electrochemically deposited onto a carbon fibre yarn. Further modification was achieved using polypyrrole (PPy) coated onto the rGO carbon fibre yarn via electropolymerisation of pyrrole with cyclic voltammetry (CV). The surface of the rGO and PPy-rGO carbon fibre electrodes were characterised using Raman spectroscopy and scanning electron microscopy. The rGO and PPy-rGO carbon fibres had a 3.5-fold and 7-fold larger electrochemical surface area compared to bare carbon fibre (calculated using the Randles-Sevcik equation). Two clearly distinguished oxidation peaks at 0.49 and 0.25 V (vs. Ag/AgCl) were observed at the rGO fibre electrode during the simultaneous detection of paracetamol and dopamine, respectively, by CV. The detection limit (3σ S/N) of the rGO carbon fibre electrode for differential pulse voltammetry (DPV) determination of paracetamol was at 21.1 and 6.0 µM for dopamine. In comparison, the simultaneous determination of paracetamol and dopamine by CV at the PPy-rGO fibre electrode gave oxidation peaks of paracetamol and dopamine at 0.55 and 0.25 V (vs. Ag/AgCl), respectively. The detection limit (3σ S/N) for paracetamol was notably improved to 3.7 µM and maintained at 6.0 µM for dopamine at the PPy-rGO carbon fibre electrode during DPV.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43157907","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 : 2021-06-15DOI: 10.3389/fsens.2021.654357
Sunil Gc, Borhan Saidul Md, Yu Zhang, D. Reed, M. Ahsan, E. Berg, Xin Sun
The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.
{"title":"Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts","authors":"Sunil Gc, Borhan Saidul Md, Yu Zhang, D. Reed, M. Ahsan, E. Berg, Xin Sun","doi":"10.3389/fsens.2021.654357","DOIUrl":"https://doi.org/10.3389/fsens.2021.654357","url":null,"abstract":"The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44181084","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 : 2021-06-14DOI: 10.3389/fsens.2021.617484
S. Balashov, J. Rocha, M. R. F. Hurtado, J. A. L. Prestes, A. F. M. de Campos, S. Moshkalev
Surface Acoustic Waves (SAW) sensors are known to be an excellent choice for the measurement of a small concentration of analytes in gas mixtures. The use of this type of sensor has been limited until now in the industrial environment due to the sensitivity of its response to temperature variations. To overcome this problem, thermal stabilization of equipment is normally used. We propose here a simple procedure of compensation of thermal drift in SAW sensors, allowing the measurements to be performed in temperature intervals of up to 20 degrees without any thermal stabilization of the sensitive element of a sensor. By monitoring the temperature of the key points of the sensor and applying the proposed polynomial compensation, it is possible to reduce the influence of thermal instabilities of the ambient temperature to the response more than four times. The method is illustrated by a temperature compensated SAW humidity sensor with a graphene oxide nanofilm as water molecules’ sensitive element. The results show enhanced performance of the sensor over a large temperature interval.
{"title":"Improved Stability and Performance of Surface Acoustic Wave Nanosensors Using a Digital Temperature Compensation","authors":"S. Balashov, J. Rocha, M. R. F. Hurtado, J. A. L. Prestes, A. F. M. de Campos, S. Moshkalev","doi":"10.3389/fsens.2021.617484","DOIUrl":"https://doi.org/10.3389/fsens.2021.617484","url":null,"abstract":"Surface Acoustic Waves (SAW) sensors are known to be an excellent choice for the measurement of a small concentration of analytes in gas mixtures. The use of this type of sensor has been limited until now in the industrial environment due to the sensitivity of its response to temperature variations. To overcome this problem, thermal stabilization of equipment is normally used. We propose here a simple procedure of compensation of thermal drift in SAW sensors, allowing the measurements to be performed in temperature intervals of up to 20 degrees without any thermal stabilization of the sensitive element of a sensor. By monitoring the temperature of the key points of the sensor and applying the proposed polynomial compensation, it is possible to reduce the influence of thermal instabilities of the ambient temperature to the response more than four times. The method is illustrated by a temperature compensated SAW humidity sensor with a graphene oxide nanofilm as water molecules’ sensitive element. The results show enhanced performance of the sensor over a large temperature interval.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46987913","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 : 2021-06-01DOI: 10.3389/fsens.2021.700967
Guangjie Han
Nowadays, Sensors have been deployed all over the daily living environment, integrated into smart phones, smart watches, and other wireless terminal devices, and become the necessities in modern daily life. With the progress of IoT (Internet of Things) and AI (artificial intelligence), more wireless sensing devices will be used to extend human senses for providing accurate and comprehensive sensory data of life activities via networking. The International Data Corporation (IDC) reports that both the number of online devices and total generated data will reach unprecedented magnitudes in 2025. Their exponential increase heralds the advent of a new era named “Internet of Everything (IoE)”. Obviously, mass data is an opportunity to develop data-driven technologies, but also a challenge for computational loading capacity. Given the transmission cost, information security, and system scalability, Sensor Network based schemes may be the current optimal solution (Akyildiz et al., 2002). The field of Sensor Networks has gone through three major reforms ranging from version 1.0 (isolated static systems) to 3.0 (invisible adaptive, self-managing systems), each of which has witnessed the revolution of IoT technology. In stage 1.0, i.e., Sensor Networks 1.0, the topics under continuous discussion are about sensor localization, intelligent management, interconnection, etc., on which most of current research products still concentrate. In other words, how to connect our physical world to the Internet through sensor networks is an everlasting hotspot. In moving forward, the fusion of sensors and networks must confront the following challenges:
如今，传感器已经部署在日常生活环境的各个角落，融入到智能手机、智能手表等无线终端设备中，成为现代人日常生活中的必需品。随着物联网(IoT)和人工智能(AI)的发展，更多的无线传感设备将用于扩展人类的感官，通过联网提供准确、全面的生命活动感官数据。国际数据公司(IDC)报告称，到2025年，在线设备的数量和生成的数据总量都将达到前所未有的规模。它们的指数级增长预示着一个名为“万物互联(IoE)”的新时代的到来。显然，海量数据为开发数据驱动技术提供了机遇，但同时也对计算负载能力提出了挑战。考虑到传输成本、信息安全和系统可扩展性，基于传感器网络的方案可能是当前的最佳解决方案(Akyildiz et al.， 2002)。传感器网络领域经历了从1.0(孤立的静态系统)到3.0(无形的自适应、自我管理系统)的三次重大变革，每一次都见证了物联网技术的革命。在1.0阶段，即传感器网络1.0，持续讨论的主题是传感器本地化、智能管理、互联互通等，目前的研究成果大多集中在这方面。换句话说，如何通过传感器网络将我们的物理世界连接到互联网是一个永恒的热点。在向前发展的过程中，传感器和网络的融合必须面对以下挑战:
{"title":"Specialty Grand Challenge: Sensor Networks","authors":"Guangjie Han","doi":"10.3389/fsens.2021.700967","DOIUrl":"https://doi.org/10.3389/fsens.2021.700967","url":null,"abstract":"Nowadays, Sensors have been deployed all over the daily living environment, integrated into smart phones, smart watches, and other wireless terminal devices, and become the necessities in modern daily life. With the progress of IoT (Internet of Things) and AI (artificial intelligence), more wireless sensing devices will be used to extend human senses for providing accurate and comprehensive sensory data of life activities via networking. The International Data Corporation (IDC) reports that both the number of online devices and total generated data will reach unprecedented magnitudes in 2025. Their exponential increase heralds the advent of a new era named “Internet of Everything (IoE)”. Obviously, mass data is an opportunity to develop data-driven technologies, but also a challenge for computational loading capacity. Given the transmission cost, information security, and system scalability, Sensor Network based schemes may be the current optimal solution (Akyildiz et al., 2002). The field of Sensor Networks has gone through three major reforms ranging from version 1.0 (isolated static systems) to 3.0 (invisible adaptive, self-managing systems), each of which has witnessed the revolution of IoT technology. In stage 1.0, i.e., Sensor Networks 1.0, the topics under continuous discussion are about sensor localization, intelligent management, interconnection, etc., on which most of current research products still concentrate. In other words, how to connect our physical world to the Internet through sensor networks is an everlasting hotspot. In moving forward, the fusion of sensors and networks must confront the following challenges:","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45442754","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 : 2021-04-26DOI: 10.3389/fsens.2021.672516
G. Domènech-Gil, I. Gràcia, C. Cané, A. Romano-Rodríguez
We report the growth of micrometer-sized In2O3 octahedral structures, which are next aligned in chains using dielectrophoresis on top of microhotplates with prepatterned electrodes and integrated heater to work as chemoresistive gas sensors. The devices are relatively fast (180 s), highly sensitive (response up to ~256%), and selective toward NO2 in humid environments, showing little response to O2 and ethanol, and being completely insensitive to CO and CH4. The here-presented fabrication method can be easily extended as a cost-effective post-process in CMOS-compatible microhotplate fabrication and, thus, represents a promising candidate for indoor and outdoor air quality monitoring devices.
{"title":"Nitrogen Dioxide Selective Sensor for Humid Environments Based on Octahedral Indium Oxide","authors":"G. Domènech-Gil, I. Gràcia, C. Cané, A. Romano-Rodríguez","doi":"10.3389/fsens.2021.672516","DOIUrl":"https://doi.org/10.3389/fsens.2021.672516","url":null,"abstract":"We report the growth of micrometer-sized In2O3 octahedral structures, which are next aligned in chains using dielectrophoresis on top of microhotplates with prepatterned electrodes and integrated heater to work as chemoresistive gas sensors. The devices are relatively fast (180 s), highly sensitive (response up to ~256%), and selective toward NO2 in humid environments, showing little response to O2 and ethanol, and being completely insensitive to CO and CH4. The here-presented fabrication method can be easily extended as a cost-effective post-process in CMOS-compatible microhotplate fabrication and, thus, represents a promising candidate for indoor and outdoor air quality monitoring devices.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41795049","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 : 2021-04-20DOI: 10.3389/fsens.2021.657931
B. Saruhan, Roussin Lontio Fomekong, S. Nahirniak
Semiconductor metal oxides (SMOxs) are widely used in gas sensors due to their excellent sensing properties, abundance, and ease of manufacture. The best examples of these sensing materials are SnO2 and TiO2 that have wide band gap and offer unique set of functional properties; the most important of which are electrical conductivity and high surface reactivity. There has been a constant development of SMOx sensor materials in the literature that has been accompanied by the improvement of their gas-sensitive properties for the gas detection. This review is dedicated to compiling of these efforts in order to mark the achievements in this area. The main material-specific aspects that strongly affect the gas sensing properties and can be controlled by the synthesis method are morphology/nanostructuring and dopants to vary crystallographic structure of MOx sensing material.
{"title":"Review: Influences of Semiconductor Metal Oxide Properties on Gas Sensing Characteristics","authors":"B. Saruhan, Roussin Lontio Fomekong, S. Nahirniak","doi":"10.3389/fsens.2021.657931","DOIUrl":"https://doi.org/10.3389/fsens.2021.657931","url":null,"abstract":"Semiconductor metal oxides (SMOxs) are widely used in gas sensors due to their excellent sensing properties, abundance, and ease of manufacture. The best examples of these sensing materials are SnO2 and TiO2 that have wide band gap and offer unique set of functional properties; the most important of which are electrical conductivity and high surface reactivity. There has been a constant development of SMOx sensor materials in the literature that has been accompanied by the improvement of their gas-sensitive properties for the gas detection. This review is dedicated to compiling of these efforts in order to mark the achievements in this area. The main material-specific aspects that strongly affect the gas sensing properties and can be controlled by the synthesis method are morphology/nanostructuring and dopants to vary crystallographic structure of MOx sensing material.","PeriodicalId":93754,"journal":{"name":"Frontiers in sensors","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42868879","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：