Pub Date : 2025-04-03DOI: 10.1109/JSEN.2025.3534229
Meng Yang;Ruotong Zhu;Wenyong Jin;Yongsheng Wang
Because of varying ecological factors such as climate, soil, temperature, and precipitation, the quality of mung beans from different origins exhibits significant differences. A fast and effective method for identifying the origin of mung beans is essential for protecting origin-specific products and safeguarding consumer rights. In this work, an electronic nose (e-nose) combined with a deep learning algorithm is proposed to identify the gas information of mung beans from different origins. First, gas information of mung beans in six renowned origins of China is detected using an e-nose system. Next, based on the time-series characteristics and cross-sensitivity in gas information, a deep feature computing module (DFCM) is proposed to adaptively compute the deep gas features along both the time and sensor directions. Finally, a deep feature computing and classification network (DFCC-Net) is designed to identify the gas information of mung beans at different origins. Through visual analysis of the gas information, ablation studies, and comparison with state-of-the-art gas classification methods, DFCC-Net demonstrates superior performance, achieving an accuracy of 97.93%, a precision of 98.09%, and a recall of 98.09%. Meanwhile, the gradient-weighted class activation mapping (Grad-CAM) visualization method is employed to highlight key gas features, further validating the effectiveness of feature computation and classification by DFCC-Net. In conclusion, the integration of the e-nose system with DFCC-Net offers an effective approach for accurately identifying the origin of mung beans and protecting origin-specific products.
{"title":"An Electronic Nose Combined With DFCC-Net for Origin Identification of Mung Beans","authors":"Meng Yang;Ruotong Zhu;Wenyong Jin;Yongsheng Wang","doi":"10.1109/JSEN.2025.3534229","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3534229","url":null,"abstract":"Because of varying ecological factors such as climate, soil, temperature, and precipitation, the quality of mung beans from different origins exhibits significant differences. A fast and effective method for identifying the origin of mung beans is essential for protecting origin-specific products and safeguarding consumer rights. In this work, an electronic nose (e-nose) combined with a deep learning algorithm is proposed to identify the gas information of mung beans from different origins. First, gas information of mung beans in six renowned origins of China is detected using an e-nose system. Next, based on the time-series characteristics and cross-sensitivity in gas information, a deep feature computing module (DFCM) is proposed to adaptively compute the deep gas features along both the time and sensor directions. Finally, a deep feature computing and classification network (DFCC-Net) is designed to identify the gas information of mung beans at different origins. Through visual analysis of the gas information, ablation studies, and comparison with state-of-the-art gas classification methods, DFCC-Net demonstrates superior performance, achieving an accuracy of 97.93%, a precision of 98.09%, and a recall of 98.09%. Meanwhile, the gradient-weighted class activation mapping (Grad-CAM) visualization method is employed to highlight key gas features, further validating the effectiveness of feature computation and classification by DFCC-Net. In conclusion, the integration of the e-nose system with DFCC-Net offers an effective approach for accurately identifying the origin of mung beans and protecting origin-specific products.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"14173-14182"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1109/JSEN.2025.3533876
Rathlavath Priyanka;L. Chandrasekar;Rameez Raja Shaik;Kumar Prasannajit Pradhan
Presents corrections to the paper, Corrections to “Label Free DNA Detection Techniques Using Dielectric Modulated FET: Inversion or Tunneling?”.
{"title":"Corrections to “Label Free DNA Detection Techniques Using Dielectric Modulated FET: Inversion or Tunneling?”","authors":"Rathlavath Priyanka;L. Chandrasekar;Rameez Raja Shaik;Kumar Prasannajit Pradhan","doi":"10.1109/JSEN.2025.3533876","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3533876","url":null,"abstract":"Presents corrections to the paper, Corrections to “Label Free DNA Detection Techniques Using Dielectric Modulated FET: Inversion or Tunneling?”.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12538-12538"},"PeriodicalIF":4.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, the particle filtering problem is investigated for nonlinear systems with correlated noise and missing measurements (MMs). By accounting for both correlated noise and MMs, a novel explicit weighting expression is presented. Based on this weighting scheme, a new bootstrap particle filtering algorithm is designed to address such influence. Furthermore, to limit the particle degradation suffered by the bootstrap particle filter (PF), a novel importance function based on the Gaussian optimal filter is presented. To perform the numerical integration required by the Gaussian optimal filter, the fifth-degree spherical-radial cubature rule (FSRCR) is used to acquire a novel importance function. Consequently, a novel high-degree cubature particle filtering algorithm is developed for these systems. Simulation experiments show that the two proposed algorithms significantly improve estimation accuracy, with notable performance gains over the existing unscented Kalman filter (KF), especially as the sample size increases.
{"title":"Bootstrap and High-Degree Cubature Particle Filters for Nonlinear Systems With Correlated Noise and Missing Measurements","authors":"Xing Zhang;Zhenrong Yang;Xiaohui Lin;Wenqian Xiang","doi":"10.1109/JSEN.2025.3548637","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3548637","url":null,"abstract":"In this article, the particle filtering problem is investigated for nonlinear systems with correlated noise and missing measurements (MMs). By accounting for both correlated noise and MMs, a novel explicit weighting expression is presented. Based on this weighting scheme, a new bootstrap particle filtering algorithm is designed to address such influence. Furthermore, to limit the particle degradation suffered by the bootstrap particle filter (PF), a novel importance function based on the Gaussian optimal filter is presented. To perform the numerical integration required by the Gaussian optimal filter, the fifth-degree spherical-radial cubature rule (FSRCR) is used to acquire a novel importance function. Consequently, a novel high-degree cubature particle filtering algorithm is developed for these systems. Simulation experiments show that the two proposed algorithms significantly improve estimation accuracy, with notable performance gains over the existing unscented Kalman filter (KF), especially as the sample size increases.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"13219-13231"},"PeriodicalIF":4.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In industrial applications, intelligent fault diagnosis technology is rapidly evolving, with deep learning-based intelligent diagnosis methods proving effective in managing and maintaining equipment. However, the variability of working conditions for mechanical equipment in actual industrial settings often presents challenges. The data collected by sensors from different working conditions or machines may exhibit significant differences in distribution. In addition, it is difficult to collect a large number of labeled samples. This article introduces a domain-adaptive meta-learning method with multilayer convolution attention, named (MCA-DAML), designed for cross-domain bearing fault diagnosis. The proposed approach involves taking the source domain data and unlabeled target domain data as inputs. Through adversarial domain adaptation (DA), the model is trained to simultaneously minimize the source domain task loss and maximize the confusion error of the domain discriminator. This dual optimization strategy encourages the model to learn shared feature representations that are effective across different domains. Multilayer convolutional attention modules are used to enhance the feature extraction capabilities of the model and suppress redundant features, which are analyzed by the prototype network for proximity to established fault prototypes, ultimately achieving accurate fault classification. Evaluated using three bearing vibration datasets without labeling the target domain samples, the average accuracy achieved was 99.86%, 96.51%, and 94.24% when performing the three cross-domain cases within the same machine and between different machines, respectively. The experimental results validate the superior performance of our method relative to other diagnostic methods.
{"title":"A Domain Adaptation Meta Learning Method With Multilayer Convolutional Attention for Cross-Domain Bearing Fault Diagnosis","authors":"Shanshan Wang;Wenkang Han;Junjie Jian;Xinyu Chang;Liang Zeng","doi":"10.1109/JSEN.2025.3546955","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3546955","url":null,"abstract":"In industrial applications, intelligent fault diagnosis technology is rapidly evolving, with deep learning-based intelligent diagnosis methods proving effective in managing and maintaining equipment. However, the variability of working conditions for mechanical equipment in actual industrial settings often presents challenges. The data collected by sensors from different working conditions or machines may exhibit significant differences in distribution. In addition, it is difficult to collect a large number of labeled samples. This article introduces a domain-adaptive meta-learning method with multilayer convolution attention, named (MCA-DAML), designed for cross-domain bearing fault diagnosis. The proposed approach involves taking the source domain data and unlabeled target domain data as inputs. Through adversarial domain adaptation (DA), the model is trained to simultaneously minimize the source domain task loss and maximize the confusion error of the domain discriminator. This dual optimization strategy encourages the model to learn shared feature representations that are effective across different domains. Multilayer convolutional attention modules are used to enhance the feature extraction capabilities of the model and suppress redundant features, which are analyzed by the prototype network for proximity to established fault prototypes, ultimately achieving accurate fault classification. Evaluated using three bearing vibration datasets without labeling the target domain samples, the average accuracy achieved was 99.86%, 96.51%, and 94.24% when performing the three cross-domain cases within the same machine and between different machines, respectively. The experimental results validate the superior performance of our method relative to other diagnostic methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"14440-14452"},"PeriodicalIF":4.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/JSEN.2025.3530977
Xianglong Xiao;Qian Gao;Ruoshan Lei;Lihui Huang;Shiqing Xu;Shilong Zhao;Xiuli Wang
A high-precision ratiometric fluorescence temperature sensor was constructed and used to achieve the real-time chip temperature monitoring. Intense green fluorescence signals at 535 and 557 nm were observed in KYW2O8:Er3/Yb3 phosphors at a low energizing power of 1.5 mW. The calibration curve between fluorescence intensity ratio (FIR) of two green fluorescence signals and temperature was built at the temperature range of 253–423 K. The fitted regression coefficient was 0.999. The maximum absolute and relative temperature sensitivity ${S}_{text {a}}$ and ${S}_{text {r}}$ are 0.0115 K$^{-{1}}$ at 423 K and 0.0145 K$^{-{1}}$ at 253 K, respectively. The temperature measurement error is only ±0.2 K. Six round cyclic heating and cooling tests indicate that the built fluorescence temperature sensor exhibits good repeatability and could realize real time and accurate measurement of chip temperature.
构建并使用高精度比率荧光温度传感器实现了芯片温度的实时监测。在 1.5 mW 的低通电功率下，KYW2O8:Er3/Yb3 荧光在 535 和 557 nm 处发出了强烈的绿色荧光信号。在 253-423 K 的温度范围内，建立了两个绿色荧光信号的荧光强度比（FIR）与温度之间的校准曲线，拟合回归系数为 0.999。在 423 K 和 253 K 温度范围内，最大绝对温度灵敏度 ${S}_{text {a}$ 和相对温度灵敏度 ${S}_{text {r}}$ 分别为 0.0115 K $^{-{1}}$ 和 0.0145 K $^{-{1}}$。六轮循环加热和冷却测试表明，所构建的荧光温度传感器具有良好的重复性，可以实现芯片温度的实时精确测量。
{"title":"A High-Precision Fluorescence Temperature Sensor Based on Er3+-/Yb3+-Doped KYW2O8 Phosphors","authors":"Xianglong Xiao;Qian Gao;Ruoshan Lei;Lihui Huang;Shiqing Xu;Shilong Zhao;Xiuli Wang","doi":"10.1109/JSEN.2025.3530977","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3530977","url":null,"abstract":"A high-precision ratiometric fluorescence temperature sensor was constructed and used to achieve the real-time chip temperature monitoring. Intense green fluorescence signals at 535 and 557 nm were observed in KYW2O8:Er3/Yb3 phosphors at a low energizing power of 1.5 mW. The calibration curve between fluorescence intensity ratio (FIR) of two green fluorescence signals and temperature was built at the temperature range of 253–423 K. The fitted regression coefficient was 0.999. The maximum absolute and relative temperature sensitivity <inline-formula> <tex-math>${S}_{text {a}}$ </tex-math></inline-formula> and <inline-formula> <tex-math>${S}_{text {r}}$ </tex-math></inline-formula> are 0.0115 K<inline-formula> <tex-math>$^{-{1}}$ </tex-math></inline-formula> at 423 K and 0.0145 K<inline-formula> <tex-math>$^{-{1}}$ </tex-math></inline-formula> at 253 K, respectively. The temperature measurement error is only ±0.2 K. Six round cyclic heating and cooling tests indicate that the built fluorescence temperature sensor exhibits good repeatability and could realize real time and accurate measurement of chip temperature.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"12653-12658"},"PeriodicalIF":4.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/JSEN.2025.3549793
Bin Li;Zongpan Li;Fan Zhang;Bing Lu;Pengxing Guo;Lei Guo;Weigang Hou
A compact dual-parameter optical fiber sensor combining surface plasmon resonance (SPR) and lossy mode resonance (LMR) technologies was proposed for the simultaneous measurement of liquid refractive index (RI) and temperature. The sensor uses an Au film deposited on one side of a D-shaped quartz no-core optical fiber to excite SPR for RI measurement, and a TiO2 film deposited on the other side, which is coated with PDMS to excite LMR for temperature sensing. Numerical simulation results show that the sensor achieves a maximum sensitivity of 16450 nm/RIU in the RI range of 1.33–1.42, and an average sensitivity of −4.36 nm/°C in the temperature range of $20~^{circ }$ C–$100~^{circ }$ C. Furthermore, by further depositing a TiO2 film on the Au film surface to enhance the electric field, the maximum RI sensitivity of the sensor increases to 21650 nm/RIU. This sensor offers a wide measurement range and excellent sensitivity, making it promising for multiparameter measurement applications.
{"title":"A Dual-Parameter Optical Fiber Sensor Based on SPR and LMR for Measuring Liquid Refractive Index and Temperature","authors":"Bin Li;Zongpan Li;Fan Zhang;Bing Lu;Pengxing Guo;Lei Guo;Weigang Hou","doi":"10.1109/JSEN.2025.3549793","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3549793","url":null,"abstract":"A compact dual-parameter optical fiber sensor combining surface plasmon resonance (SPR) and lossy mode resonance (LMR) technologies was proposed for the simultaneous measurement of liquid refractive index (RI) and temperature. The sensor uses an Au film deposited on one side of a D-shaped quartz no-core optical fiber to excite SPR for RI measurement, and a TiO2 film deposited on the other side, which is coated with PDMS to excite LMR for temperature sensing. Numerical simulation results show that the sensor achieves a maximum sensitivity of 16450 nm/RIU in the RI range of 1.33–1.42, and an average sensitivity of −4.36 nm/°C in the temperature range of <inline-formula> <tex-math>$20~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$100~^{circ }$ </tex-math></inline-formula>C. Furthermore, by further depositing a TiO2 film on the Au film surface to enhance the electric field, the maximum RI sensitivity of the sensor increases to 21650 nm/RIU. This sensor offers a wide measurement range and excellent sensitivity, making it promising for multiparameter measurement applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"12907-12914"},"PeriodicalIF":4.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the most crucial medium for multimedia presentation, liquid crystal display (LCD) is integral to numerous industries, making precise defect detection essential to ensure display quality and user experience. However, high-accuracy defect detection of LCDs remains a significant challenge due to large-scale variations and high interclass similarity, especially under the setting of few-shot learning. To address these challenges, we propose a few-shot defect detection network, namely, HiSCAD-Net. Specifically, we design an auxiliary branch for hierarchical scale enhancement, which introduces additional objectness and classification constraints based on object pyramid sampling. Moreover, to tackle the misclassification caused by interclass similarity, we introduce an object-level contrastive encoding (OCE) to encourage class-discriminative feature learning, which enforces zero distance between objects of the same class, while ensuring that the distance between objects of different classes remains above a predefined threshold. Finally, we propose an adaptive decoupling module (ADM) to mitigate interference between classification and regression tasks given limited training samples, thereby improving both the tasks during decoding. To support benchmarking in few-shot LCD (FSLCD) defect detection, we propose a new dataset named FSLCD. Experimental results on the FSLCD, NEU-DET, and PKU-Market-Phone datasets demonstrate that the proposed model outperforms 18 state-of-the-art methods, validating its effectiveness and generalizability. Notably, in the ten-shot setting, our model achieved a mean average precision (mAP) of 62.0%, surpassing the state-of-the-art by 10.4%.
{"title":"Hierarchical Scale Enhancement Network With Contrast Encoding for Few-Shot Liquid Crystal Display Defect Detection","authors":"Sijie Luo;Biyuan Liu;Huaixin Chen;Zhixi Wang;Ruoyu Yang;Ying Huang","doi":"10.1109/JSEN.2025.3549521","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3549521","url":null,"abstract":"As the most crucial medium for multimedia presentation, liquid crystal display (LCD) is integral to numerous industries, making precise defect detection essential to ensure display quality and user experience. However, high-accuracy defect detection of LCDs remains a significant challenge due to large-scale variations and high interclass similarity, especially under the setting of few-shot learning. To address these challenges, we propose a few-shot defect detection network, namely, HiSCAD-Net. Specifically, we design an auxiliary branch for hierarchical scale enhancement, which introduces additional objectness and classification constraints based on object pyramid sampling. Moreover, to tackle the misclassification caused by interclass similarity, we introduce an object-level contrastive encoding (OCE) to encourage class-discriminative feature learning, which enforces zero distance between objects of the same class, while ensuring that the distance between objects of different classes remains above a predefined threshold. Finally, we propose an adaptive decoupling module (ADM) to mitigate interference between classification and regression tasks given limited training samples, thereby improving both the tasks during decoding. To support benchmarking in few-shot LCD (FSLCD) defect detection, we propose a new dataset named FSLCD. Experimental results on the FSLCD, NEU-DET, and PKU-Market-Phone datasets demonstrate that the proposed model outperforms 18 state-of-the-art methods, validating its effectiveness and generalizability. Notably, in the ten-shot setting, our model achieved a mean average precision (mAP) of 62.0%, surpassing the state-of-the-art by 10.4%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"13160-13174"},"PeriodicalIF":4.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydrogen (H2) is a typical gas generated by the thermal runaway (TR) fault of lithium-ion batteries (LIBs) and a typical gas for TR fault diagnosis. Fast detection and high sensitivity of hydrogen sensors have practical significance. In this article, the hydrothermal method was used to prepare Co3O4 nanosheets, and Ag- and Bi-modified Co3O4 composite was also successfully prepared. X-ray diffraction (XRD), scanning electron microscopy (SEM), TEM, and X-ray photoelectron spectroscopy (XPS) were used to characterize and analyze the crystalline phase structure, micromorphology, and physical equality properties of the material. Then the gas sensing properties of AgBi/Co3O4 composite on H2 were investigated. The experimental results showed that the optimum operating temperature of the AgBi/Co3O4 sensor for H2 is $160~^{circ }$ C. At this temperature, the AgBi/Co3O4 sensor has the best hydrogen sensing performance, with a response value of 2.6–400 ppm H2 and a response recovery time of 19/13 s, in addition, it has good stability and selectivity. Through first-principles calculation, the sensing mechanism of AgBi/Co3O4 was further explored. The results showed that the enhancement of AgBi/Co3O4 hydrogen sensing performance was the result of the synergistic catalytic action of Ag and Bi bimetals.
{"title":"Hydrogen Sensor for LIB Thermal Runaway Based on Ag-Bi-Modified Co3O4 Nanosheets: Experimental and DFT Calculation","authors":"Yong Zhang;Jieshuo Zhai;Peilin Jia;Xingyan Shao;Xinyi Ji;Gongao Jiao;Dongzhi Zhang","doi":"10.1109/JSEN.2025.3547937","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3547937","url":null,"abstract":"Hydrogen (H2) is a typical gas generated by the thermal runaway (TR) fault of lithium-ion batteries (LIBs) and a typical gas for TR fault diagnosis. Fast detection and high sensitivity of hydrogen sensors have practical significance. In this article, the hydrothermal method was used to prepare Co3O4 nanosheets, and Ag- and Bi-modified Co3O4 composite was also successfully prepared. X-ray diffraction (XRD), scanning electron microscopy (SEM), TEM, and X-ray photoelectron spectroscopy (XPS) were used to characterize and analyze the crystalline phase structure, micromorphology, and physical equality properties of the material. Then the gas sensing properties of AgBi/Co3O4 composite on H2 were investigated. The experimental results showed that the optimum operating temperature of the AgBi/Co3O4 sensor for H2 is <inline-formula> <tex-math>$160~^{circ }$ </tex-math></inline-formula>C. At this temperature, the AgBi/Co3O4 sensor has the best hydrogen sensing performance, with a response value of 2.6–400 ppm H2 and a response recovery time of 19/13 s, in addition, it has good stability and selectivity. Through first-principles calculation, the sensing mechanism of AgBi/Co3O4 was further explored. The results showed that the enhancement of AgBi/Co3O4 hydrogen sensing performance was the result of the synergistic catalytic action of Ag and Bi bimetals.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"12599-12608"},"PeriodicalIF":4.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","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：