{"title":"一种具有三维夹层结构和热漂移抑制的柔性双峰压力-温度传感器","authors":"Baichuan Sun;Gaobin Xu;Zhaohui Yang;Cunhe Guan;Shirong Chen;Xing Chen;Yuanming Ma;Yongqiang Yu;Jianguo Feng","doi":"10.1109/JSEN.2025.3556426","DOIUrl":null,"url":null,"abstract":"Flexible bimodal pressure-temperature sensors, particularly those using piezoresistive/thermosensitive mechanisms, face challenges such as spatial signal interference and complex signal processing. Additionally, thermal drift in conductive fillers like carbon nanotubes (CNTs), although often negligible in narrow-range applications like human monitoring, significantly affects sensor performance and limits broader industrial applicability. In order to address these issues, this study proposes a novel flexible bimodal pressure-temperature sensor with a 3-D “sandwich” structure, enabling simultaneous monitoring of temperature (<inline-formula> <tex-math>$20~^{\\circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$125~^{\\circ }$ </tex-math></inline-formula>C) and pressure (0.02–500 kPa). The temperature sensor employs a negative temperature coefficient (NTC) thermistor, while the pressure sensor features a novel composite design integrating CNT materials with a sponge-microhemisphere structure. The 3-D layered structure and innovative pressure sensor design resolve spatial signal interference, enhance mechanical robustness, and achieve high sensitivity (2.714 <inline-formula> <tex-math>$\\text{kPa}^{-{1}}$ </tex-math></inline-formula>), fast response (~78 ms), wide monitoring range, and long-term durability (over 10000 cycles). The thermal drift of CNT-based piezoresistive fillers is, furthermore, effectively mitigated using a backpropagation neural network (BPNN), which also addresses the complex signal processing issue, achieving an exceptional <inline-formula> <tex-math>${R} ^{{2}}$ </tex-math></inline-formula> value of 0.9975. This compensation method provides valuable guidance for addressing thermal drift in similar conductive fillers and demonstrates broad applicability. The proposed sensor design significantly broadens the application scope of flexible bimodal sensors, offering robust solutions for industrial scenarios such as thermal runaway detection in lithium-ion batteries, along with potential uses in environmental monitoring and smart manufacturing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18123-18135"},"PeriodicalIF":4.3000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Flexible Bimodal Pressure–Temperature Sensor With 3-D Sandwich Structure and Thermal Drift Mitigation\",\"authors\":\"Baichuan Sun;Gaobin Xu;Zhaohui Yang;Cunhe Guan;Shirong Chen;Xing Chen;Yuanming Ma;Yongqiang Yu;Jianguo Feng\",\"doi\":\"10.1109/JSEN.2025.3556426\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flexible bimodal pressure-temperature sensors, particularly those using piezoresistive/thermosensitive mechanisms, face challenges such as spatial signal interference and complex signal processing. Additionally, thermal drift in conductive fillers like carbon nanotubes (CNTs), although often negligible in narrow-range applications like human monitoring, significantly affects sensor performance and limits broader industrial applicability. In order to address these issues, this study proposes a novel flexible bimodal pressure-temperature sensor with a 3-D “sandwich” structure, enabling simultaneous monitoring of temperature (<inline-formula> <tex-math>$20~^{\\\\circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$125~^{\\\\circ }$ </tex-math></inline-formula>C) and pressure (0.02–500 kPa). The temperature sensor employs a negative temperature coefficient (NTC) thermistor, while the pressure sensor features a novel composite design integrating CNT materials with a sponge-microhemisphere structure. The 3-D layered structure and innovative pressure sensor design resolve spatial signal interference, enhance mechanical robustness, and achieve high sensitivity (2.714 <inline-formula> <tex-math>$\\\\text{kPa}^{-{1}}$ </tex-math></inline-formula>), fast response (~78 ms), wide monitoring range, and long-term durability (over 10000 cycles). The thermal drift of CNT-based piezoresistive fillers is, furthermore, effectively mitigated using a backpropagation neural network (BPNN), which also addresses the complex signal processing issue, achieving an exceptional <inline-formula> <tex-math>${R} ^{{2}}$ </tex-math></inline-formula> value of 0.9975. This compensation method provides valuable guidance for addressing thermal drift in similar conductive fillers and demonstrates broad applicability. 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引用次数: 0
摘要
柔性双峰压力-温度传感器,特别是那些使用压阻/热敏机制的传感器,面临着空间信号干扰和复杂信号处理等挑战。此外,碳纳米管(CNTs)等导电填料中的热漂移,虽然在人体监测等窄范围应用中通常可以忽略不计,但会显著影响传感器性能并限制更广泛的工业适用性。为了解决这些问题,本研究提出了一种具有三维“三明治”结构的新型柔性双峰压力-温度传感器,可以同时监测温度($20~^{\circ}$ C - $125~^{\circ}$ C)和压力(0.02-500 kPa)。温度传感器采用负温度系数(NTC)热敏电阻,而压力传感器采用新颖的复合设计,将碳纳米管材料与海绵状微半球结构集成在一起。三维分层结构和创新的压力传感器设计解决了空间信号干扰,增强了机械鲁棒性,并实现了高灵敏度(2.714 $\text{kPa}^{-{1}}$)、快速响应(~78 ms)、宽监测范围和长期耐用性(超过10000次循环)。此外,利用反向传播神经网络(BPNN)有效地缓解了基于碳纳米管的压阻填料的热漂移,该网络还解决了复杂的信号处理问题,实现了异常的${R} ^{{2}}$值0.9975。这种补偿方法为解决类似导电填料的热漂移问题提供了有价值的指导,并证明了广泛的适用性。提出的传感器设计大大拓宽了柔性双峰传感器的应用范围,为锂离子电池的热失控检测等工业场景提供了强大的解决方案,以及环境监测和智能制造的潜在用途。
A Flexible Bimodal Pressure–Temperature Sensor With 3-D Sandwich Structure and Thermal Drift Mitigation
Flexible bimodal pressure-temperature sensors, particularly those using piezoresistive/thermosensitive mechanisms, face challenges such as spatial signal interference and complex signal processing. Additionally, thermal drift in conductive fillers like carbon nanotubes (CNTs), although often negligible in narrow-range applications like human monitoring, significantly affects sensor performance and limits broader industrial applicability. In order to address these issues, this study proposes a novel flexible bimodal pressure-temperature sensor with a 3-D “sandwich” structure, enabling simultaneous monitoring of temperature ($20~^{\circ }$ C–$125~^{\circ }$ C) and pressure (0.02–500 kPa). The temperature sensor employs a negative temperature coefficient (NTC) thermistor, while the pressure sensor features a novel composite design integrating CNT materials with a sponge-microhemisphere structure. The 3-D layered structure and innovative pressure sensor design resolve spatial signal interference, enhance mechanical robustness, and achieve high sensitivity (2.714 $\text{kPa}^{-{1}}$ ), fast response (~78 ms), wide monitoring range, and long-term durability (over 10000 cycles). The thermal drift of CNT-based piezoresistive fillers is, furthermore, effectively mitigated using a backpropagation neural network (BPNN), which also addresses the complex signal processing issue, achieving an exceptional ${R} ^{{2}}$ value of 0.9975. This compensation method provides valuable guidance for addressing thermal drift in similar conductive fillers and demonstrates broad applicability. The proposed sensor design significantly broadens the application scope of flexible bimodal sensors, offering robust solutions for industrial scenarios such as thermal runaway detection in lithium-ion batteries, along with potential uses in environmental monitoring and smart manufacturing.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
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-Sensors in Industrial Practice