Pub Date : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956629
Fei Chang, Yunqiang Duan, Min Liu, Mingyu Dong
High-quality image acquisition under uncertain imaging conditions (such as uneven and varied illuminations, various viewpoints and different object distances, etc.) is a very challenging task. However, the imaging quality of industrial vision inspection system is vital to subsequent image processing, especially for those challenging detection tasks, such as tiny defect inspection of paint car-body surfaces. In order to overcome the challenge of image acquisition due to uncertain imaging conditions, a two-stage adaptive illumination adjustment method is proposed to handle the uncertainty caused by diversities of lighting, viewpoint and object distance. Our algorithm framework has been implemented and applied to the mobile inspection system deployed in a car painting factory for tiny defect detection of paint car-body surfaces. The efficiency and effectiveness of our method has been validated by the actual industrial application. As a result, the proposed coarse-to-fine framework can be viewed as an adaptive hard-adjustment solution for industrial vision inspection system under uncertain imaging conditions.
{"title":"Coarse-to-Fine Adaptive Illumination Hard-Adjustment for Vision Inspection System Under Uncertain Imaging Conditions","authors":"Fei Chang, Yunqiang Duan, Min Liu, Mingyu Dong","doi":"10.1109/SENSORS43011.2019.8956629","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956629","url":null,"abstract":"High-quality image acquisition under uncertain imaging conditions (such as uneven and varied illuminations, various viewpoints and different object distances, etc.) is a very challenging task. However, the imaging quality of industrial vision inspection system is vital to subsequent image processing, especially for those challenging detection tasks, such as tiny defect inspection of paint car-body surfaces. In order to overcome the challenge of image acquisition due to uncertain imaging conditions, a two-stage adaptive illumination adjustment method is proposed to handle the uncertainty caused by diversities of lighting, viewpoint and object distance. Our algorithm framework has been implemented and applied to the mobile inspection system deployed in a car painting factory for tiny defect detection of paint car-body surfaces. The efficiency and effectiveness of our method has been validated by the actual industrial application. As a result, the proposed coarse-to-fine framework can be viewed as an adaptive hard-adjustment solution for industrial vision inspection system under uncertain imaging conditions.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"44 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87333583","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956801
Ozberk Ozturk, M. Yapici
In this study, we report, for the first time, wearable graphene textile electrodes for monitoring of muscular activity and surface electromyography (sEMG) applications. The feasibility of graphene textiles in wearable muscular monitoring was successfully demonstrated by the acquisition of sEMG signals with wearable graphene textiles, and their performance was benchmarked against commercial, wet Ag/AgCl electrodes. Comparisons were performed in terms of signal-to-noise ratio (SNR), cross correlation and sensitivity to power-line interference. Despite their larger susceptibility to power line interference, graphene textile electrodes displayed excellent similarity with Ag/AgCl electrodes in terms of signal-to-noise ratio (SNR) and signal morphology; with correlation values reaching up to 97 % for sEMG signals acquired from the biceps brachii muscle.
{"title":"Muscular Activity Monitoring and Surface Electromyography (sEMG) with Graphene Textiles","authors":"Ozberk Ozturk, M. Yapici","doi":"10.1109/SENSORS43011.2019.8956801","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956801","url":null,"abstract":"In this study, we report, for the first time, wearable graphene textile electrodes for monitoring of muscular activity and surface electromyography (sEMG) applications. The feasibility of graphene textiles in wearable muscular monitoring was successfully demonstrated by the acquisition of sEMG signals with wearable graphene textiles, and their performance was benchmarked against commercial, wet Ag/AgCl electrodes. Comparisons were performed in terms of signal-to-noise ratio (SNR), cross correlation and sensitivity to power-line interference. Despite their larger susceptibility to power line interference, graphene textile electrodes displayed excellent similarity with Ag/AgCl electrodes in terms of signal-to-noise ratio (SNR) and signal morphology; with correlation values reaching up to 97 % for sEMG signals acquired from the biceps brachii muscle.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"24 11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91052665","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956734
Reza Nouri, Zifan Tang, W. Guan
Nanopore sensor conceptually represents an ideal single molecule counting device due to its unique partitioning-free, label-free electronic sensing. Existing theories and experiments have shown that sample concentration is proportional to the molecule translocation rate. However, a detailed nanopore geometry and size characterization or a calibration curve of concentration standards are often required for quantifying the unknown sample. In this work, we proposed and validated a calibration-free nanopore single molecule digital counting method for isolated molecule quantification. With the background ions as the in-situ references, the molecule translocation rates can be normalized to the ion translocation rates (baseline current). This in-situ reference alleviates the requirement for knowing the nanopore geometry and size or generating a calibration curve. In recognition of this effect, we developed a quantitative model for molecule quantification without the need for prior knowledge of experimental conditions such as nanopore geometry, size, and applied voltage. This model was experimentally validated for different nanopores and DNA molecules with different sizes. We anticipate this calibration-free digital counting approach would provide a new avenue for nanopore-based molecule sensing.
{"title":"Calibration-Free Electrical Quantification of Single Molecules Using Nanopore Digital Counting","authors":"Reza Nouri, Zifan Tang, W. Guan","doi":"10.1109/SENSORS43011.2019.8956734","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956734","url":null,"abstract":"Nanopore sensor conceptually represents an ideal single molecule counting device due to its unique partitioning-free, label-free electronic sensing. Existing theories and experiments have shown that sample concentration is proportional to the molecule translocation rate. However, a detailed nanopore geometry and size characterization or a calibration curve of concentration standards are often required for quantifying the unknown sample. In this work, we proposed and validated a calibration-free nanopore single molecule digital counting method for isolated molecule quantification. With the background ions as the in-situ references, the molecule translocation rates can be normalized to the ion translocation rates (baseline current). This in-situ reference alleviates the requirement for knowing the nanopore geometry and size or generating a calibration curve. In recognition of this effect, we developed a quantitative model for molecule quantification without the need for prior knowledge of experimental conditions such as nanopore geometry, size, and applied voltage. This model was experimentally validated for different nanopores and DNA molecules with different sizes. We anticipate this calibration-free digital counting approach would provide a new avenue for nanopore-based molecule sensing.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"77 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91096656","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956740
Qi Zhao, X. Qian, Xiaohao Wang, Liwei Lin
Environmental sensing is an important task in the development of infrastructures for the applications of the Internet of things. In this work, we introduce a flexible humidity sensor based on electrochemically polymerized polypyrrole to absorb or desorb water vapors to detect relative humidity (RH). A test platform was built to mix the different amounts of dry and wet air for the relative humidity ranging from 15% to 95%. Experimental results show that the response time from 20% to 90% RH of the sensor was 505 seconds and the recovery time from 90% to 20% RH was 328 seconds with good repeatability during three cycles. The wide dynamic range, excellent repeatability and reasonable response time make the sensor applicable for home monitoring applications. Furthermore, the flexible sensor is suitable for embedding into wallpaper or decorations for promising future systems to map the environmental status of buildings and homes.
{"title":"Flexible Humidity Sensor Based on Electrochemically Polymerized Polypyrrole","authors":"Qi Zhao, X. Qian, Xiaohao Wang, Liwei Lin","doi":"10.1109/SENSORS43011.2019.8956740","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956740","url":null,"abstract":"Environmental sensing is an important task in the development of infrastructures for the applications of the Internet of things. In this work, we introduce a flexible humidity sensor based on electrochemically polymerized polypyrrole to absorb or desorb water vapors to detect relative humidity (RH). A test platform was built to mix the different amounts of dry and wet air for the relative humidity ranging from 15% to 95%. Experimental results show that the response time from 20% to 90% RH of the sensor was 505 seconds and the recovery time from 90% to 20% RH was 328 seconds with good repeatability during three cycles. The wide dynamic range, excellent repeatability and reasonable response time make the sensor applicable for home monitoring applications. Furthermore, the flexible sensor is suitable for embedding into wallpaper or decorations for promising future systems to map the environmental status of buildings and homes.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"111 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73317727","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956897
T. Talala, I. Nissinen
Two methods were developed to compensate for the timing skew of CMOS SPAD line sensors used for time-resolving Raman spectroscopy. Both methods were tested using a time-resolving Raman spectrometer built around a 256-channel CMOS SPAD line sensor. As an example, Raman spectrum of highly fluorescent sesame seed oil was measured. Most of the distortion in the measured spectrum was caused by the timing skew and about 75 % of it could be removed by using either of the methods presented.
{"title":"Timing Skew Compensation Methods for CMOS SPAD Line Sensors Used for Raman Spectroscopy","authors":"T. Talala, I. Nissinen","doi":"10.1109/SENSORS43011.2019.8956897","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956897","url":null,"abstract":"Two methods were developed to compensate for the timing skew of CMOS SPAD line sensors used for time-resolving Raman spectroscopy. Both methods were tested using a time-resolving Raman spectrometer built around a 256-channel CMOS SPAD line sensor. As an example, Raman spectrum of highly fluorescent sesame seed oil was measured. Most of the distortion in the measured spectrum was caused by the timing skew and about 75 % of it could be removed by using either of the methods presented.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"22 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72731750","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956862
Haoyan Liu, E. Sanchez, J. Parkerson, Alexander Nelson
Machine Learning and artificial intelligence play major roles in understanding human activity through various classification and regression tasks. However, for many lowresource devices, high computation cost resulting from the construction of AI models may limit their applications. To that end, this work explores gesture recognition through a low-cost capacitive sensor matrix overlayed on a rehabilitation activity table. For gesture recognition, a convolutional long short-term memory (C-LSTM) neural network structure is applied and hyper-parameters are varied to determine what resources are necessary to perform classification tasks. The 8 X 8 mutual capacitive sensor array (CSA) is constructed with low-cost copper adhesive. The designed capacitive sensors capture hand motions performed by patients during rehabilitative exercise. The motions cause changes in the electric field that is quantified through sampling the changing capacitance between the copper tape electrodes. An MSP430 MCU computes the capacitance-todigital conversion at a 50 Hz sampling rate. To identify low computation cost models for the C-LSTM neural network, we evaluate different numbers of capacitor sensors, kernels, convolutional layers, and hidden nodes. Six subjects performed 1200 gestures, and the accuracy metrics are calculated using fivefold cross-validation.
{"title":"Gesture Classification with Low-Cost Capacitive Sensor Array for Upper Extremity Rehabilitation","authors":"Haoyan Liu, E. Sanchez, J. Parkerson, Alexander Nelson","doi":"10.1109/SENSORS43011.2019.8956862","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956862","url":null,"abstract":"Machine Learning and artificial intelligence play major roles in understanding human activity through various classification and regression tasks. However, for many lowresource devices, high computation cost resulting from the construction of AI models may limit their applications. To that end, this work explores gesture recognition through a low-cost capacitive sensor matrix overlayed on a rehabilitation activity table. For gesture recognition, a convolutional long short-term memory (C-LSTM) neural network structure is applied and hyper-parameters are varied to determine what resources are necessary to perform classification tasks. The 8 X 8 mutual capacitive sensor array (CSA) is constructed with low-cost copper adhesive. The designed capacitive sensors capture hand motions performed by patients during rehabilitative exercise. The motions cause changes in the electric field that is quantified through sampling the changing capacitance between the copper tape electrodes. An MSP430 MCU computes the capacitance-todigital conversion at a 50 Hz sampling rate. To identify low computation cost models for the C-LSTM neural network, we evaluate different numbers of capacitor sensors, kernels, convolutional layers, and hidden nodes. Six subjects performed 1200 gestures, and the accuracy metrics are calculated using fivefold cross-validation.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74515826","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956617
M. Eggimann, Jonas Erb, Philipp Mayer, M. Magno, L. Benini
This work proposes a low-power high-accuracy embedded hand-gesture recognition using low power short-range radar sensors. The hardware and software match the requirements for battery-operated wearable devices. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 45723 parameters, yielding a memory footprint of only 91kB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20210 gesture instances. On the 11 hands, gestures and an accuracy of 87% (26 users) and 92% (single user) have been achieved. Furthermore, the prediction algorithm has been implemented in the GAP8 Parallel Ultra-Low-Power processor by GreenWaves Technologies, showing that live-prediction is feasible with only 21mW of power consumption for the full gesture prediction neural network.
{"title":"Low Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors","authors":"M. Eggimann, Jonas Erb, Philipp Mayer, M. Magno, L. Benini","doi":"10.1109/SENSORS43011.2019.8956617","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956617","url":null,"abstract":"This work proposes a low-power high-accuracy embedded hand-gesture recognition using low power short-range radar sensors. The hardware and software match the requirements for battery-operated wearable devices. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 45723 parameters, yielding a memory footprint of only 91kB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20210 gesture instances. On the 11 hands, gestures and an accuracy of 87% (26 users) and 92% (single user) have been achieved. Furthermore, the prediction algorithm has been implemented in the GAP8 Parallel Ultra-Low-Power processor by GreenWaves Technologies, showing that live-prediction is feasible with only 21mW of power consumption for the full gesture prediction neural network.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"27 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75062401","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956945
Q. Shi, Min Wang, Shuang Song, M. Meng
Magnetically actuated wireless capsule robot has been a promising medical apparatus for minimally invasive examination and operation in the gastrointestinal tract. Position and orientation information of the robot are essential for an effective and safe feedback control. However, simultaneous actuation and localization is still a challenge. In our previous work, we proposed a multi-magnet based method. To further improve the tracking accuracy, in this paper we propose a method which utilizes the prior known pose information of the external actuation magnet. Moreover, the method also can simplify the calculation. A magnetic sensor array is used to sample the magnetic field from the external magnet and internal magnet. By subtracting the magnetic field of the external magnet according to the prior pose information, pose of the internal magnet then can be estimated by using nonlinear optimization algorithm. Experimental results verified the proposed method.
{"title":"An Improved Simultaneously Magnetic Actuation and Localization Method based on Magnetic Sensor Array","authors":"Q. Shi, Min Wang, Shuang Song, M. Meng","doi":"10.1109/SENSORS43011.2019.8956945","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956945","url":null,"abstract":"Magnetically actuated wireless capsule robot has been a promising medical apparatus for minimally invasive examination and operation in the gastrointestinal tract. Position and orientation information of the robot are essential for an effective and safe feedback control. However, simultaneous actuation and localization is still a challenge. In our previous work, we proposed a multi-magnet based method. To further improve the tracking accuracy, in this paper we propose a method which utilizes the prior known pose information of the external actuation magnet. Moreover, the method also can simplify the calculation. A magnetic sensor array is used to sample the magnetic field from the external magnet and internal magnet. By subtracting the magnetic field of the external magnet according to the prior pose information, pose of the internal magnet then can be estimated by using nonlinear optimization algorithm. Experimental results verified the proposed method.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75251253","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956586
J. D. Tocco, C. Massaroni, N. Stefano, D. Formica, E. Schena
We present an easy to wear and use wearable system to monitor wrist and elbow movements in musicians. The system is based on two piezoresistive sensors embedded into a garment. Pilot tests on an adult double-bass player were carried out to assess the feasibility of the device for monitoring bowing technique. Results show promising performances in identifying string changes and bow strokes. The proposed system allows for studying regularity and timing of bowing movements, thus being potentially useful in learning contexts, especially with beginner musicians.
{"title":"Wearable system based on piezoresistive sensors for monitoring bowing technique in musicians","authors":"J. D. Tocco, C. Massaroni, N. Stefano, D. Formica, E. Schena","doi":"10.1109/SENSORS43011.2019.8956586","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956586","url":null,"abstract":"We present an easy to wear and use wearable system to monitor wrist and elbow movements in musicians. The system is based on two piezoresistive sensors embedded into a garment. Pilot tests on an adult double-bass player were carried out to assess the feasibility of the device for monitoring bowing technique. Results show promising performances in identifying string changes and bow strokes. The proposed system allows for studying regularity and timing of bowing movements, thus being potentially useful in learning contexts, especially with beginner musicians.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"175 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77651281","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 : 2019-10-01DOI: 10.1109/SENSORS43011.2019.8956523
Jeonhyeong Park, I. Jang, Hoe-Joon Kim
This paper reports the fabrication and characterization of crumpled multi-walled carbon nanotube (CNT) thin film heater and its application towards hydrogen gas sensing. We have fabricated MWCNTs thin film heater by a simple spray coating and thermally shrinking the polystyrene (PS) substrate. Thermal shrinkage results crumpled CNTs with closely packed junctions, leading to a higher heating temperature at a given input voltage. Such efficient heating capabilities of the crumpled CNT heater are favorable for hydrogen gas sensing with good desorption characteristics. Our results show that higher operating temperatures result in better measurement sensitivities. In addition, the heating performance and temperature coefficient of resistance (TCR) of CNT heaters are analyzed for an accurate temperature control. The suggested crumpled CNT heaters can be applied for low-voltage gas sensing platforms.
{"title":"Crumpled Carbon Nanotube Thin Film Heaters for High Sensitivity Hydrogen Sensing","authors":"Jeonhyeong Park, I. Jang, Hoe-Joon Kim","doi":"10.1109/SENSORS43011.2019.8956523","DOIUrl":"https://doi.org/10.1109/SENSORS43011.2019.8956523","url":null,"abstract":"This paper reports the fabrication and characterization of crumpled multi-walled carbon nanotube (CNT) thin film heater and its application towards hydrogen gas sensing. We have fabricated MWCNTs thin film heater by a simple spray coating and thermally shrinking the polystyrene (PS) substrate. Thermal shrinkage results crumpled CNTs with closely packed junctions, leading to a higher heating temperature at a given input voltage. Such efficient heating capabilities of the crumpled CNT heater are favorable for hydrogen gas sensing with good desorption characteristics. Our results show that higher operating temperatures result in better measurement sensitivities. In addition, the heating performance and temperature coefficient of resistance (TCR) of CNT heaters are analyzed for an accurate temperature control. The suggested crumpled CNT heaters can be applied for low-voltage gas sensing platforms.","PeriodicalId":6710,"journal":{"name":"2019 IEEE SENSORS","volume":"280 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76422361","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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