Direct stamping multifunctional tactile sensor for pressure and temperature sensing

Liang, Binghao; Huang, Bingfang; He, Junkai; Yang, Rongliang; Zhao, Chengchun; Yang, Bo‐Ru; Cao, Anyuan; Tang, Zikang; Gui, Xuchun

doi:10.1007/s12274-021-3906-x

Cited by 26 publications

(14 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The triple-mode sensor exhibited excellent sensitivities of 19.3 kPa −1 for pressure, 0.0034 °C −1 for temperature, and fast UV response/recovery time of 0.8/0.9 s. Additionally, the micro-supercapacitor displayed a high volumetric capacitance of 148.25 F cm −3 and good cycle stability (93.75%, after 6000 cycles). Later, many similar multi-mode sensors have been developed [ 144 , 145 , 146 , 147 , 148 , 149 ], but the cross-talk between the measured parameters will lead to poor accuracy of single parameter measurements [ 118 ]. Therefore, the system integrated with multiple sensors shows advantages.…”

Section: Energy-storage-device-integrated Sensing Systemsmentioning

confidence: 99%

Recent Progress of Energy-Storage-Device-Integrated Sensing Systems

2023

View full text Add to dashboard Cite

With the rapid prosperity of the Internet of things, intelligent human–machine interaction and health monitoring are becoming the focus of attention. Wireless sensing systems, especially self-powered sensing systems that can work continuously and sustainably for a long time without an external power supply have been successfully explored and developed. Yet, the system integrated by energy-harvester needs to be exposed to a specific energy source to drive the work, which provides limited application scenarios, low stability, and poor continuity. Integrating the energy storage unit and sensing unit into a single system may provide efficient ways to solve these above problems, promoting potential applications in portable and wearable electronics. In this review, we focus on recent advances in energy-storage-device-integrated sensing systems for wearable electronics, including tactile sensors, temperature sensors, chemical and biological sensors, and multifunctional sensing systems, because of their universal utilization in the next generation of smart personal electronics. Finally, the future perspectives of energy-storage-device-integrated sensing systems are discussed.

show abstract

Section: Energy-storage-device-integrated Sensing Systemsmentioning

confidence: 99%

Recent Progress of Energy-Storage-Device-Integrated Sensing Systems

2023

View full text Add to dashboard Cite

show abstract

“…With the development of advanced IoT technology, intelligent medical sensors which can realize the monitoring of various biosignals are increasingly vital for the early diagnosis or prevention of disease. Flexible pressure sensors have come into the limelight due to their broad application prospects in electronic skin, health monitoring, human–machine interfaces, and IoTs. − Many extensive efforts have been devoted to develop high-performance flexible pressure sensors based on different transmission mechanisms, such as piezoresistive, − piezoelectric, , capacitive, − and triboelectric sensors. , Among these varied types of sensors, capacitive pressure sensors (CPSs) are more appealing due to the advantages of the simple structure, rapid response, uncomplicated signal acquisition, easy large-area fabrication, low energy consumption, and so forth . To meet the need of multifunctional application scenarios, a lot of efforts have been made to develop flexible CPSs with high sensitivity, low detection limit, superior stability, good linearity, and broad range. − According to the formula C = ε 0 ε r A / d , causing a change in capacitance ( C ) can be realized by changing the relative permittivity of the dielectric (ε r ), the interval between two electrodes ( d ), or their relative area ( A ).…”

Section: Introductionmentioning

confidence: 99%

Capacitive Sensors with Hybrid Dielectric Structures and High Sensitivity over a Wide Pressure Range for Monitoring Biosignals

Feng

Wang

et al. 2023

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Various dielectrics with porous structures or high dielectric constants have been designed to improve the sensitivity of capacitive pressure sensors (CPSs), but this strategy has only been effective for the low-pressure range. Here, a hierarchical gradient hybrid dielectric, composed of low-permittivity (low-k) polydimethylsiloxane (PDMS) foam with low Young's modulus (low-E) and highpermittivity (high-k) MWCNT/PDMS foam with high Young's modulus (high-E), is designed to develop a CPS for monitoring biosignals over a wide force range. The foam-like structure with hybrid permittivity (low-k + high-k) is facilitated to improve the sensitivity, while the hierarchical structure with gradient Young's modulus (low-E + high-E) contributes to broadening the pressure sensing range. With the hierarchical gradient hybrid structure, the flexible pressure sensor achieves an enhanced sensitivity of 2.155 kPa −1 , a wide pressure range (up to 500 kPa), a minimum detection limit (50 Pa), and an excellent durability (>2500 cycles). As a demonstration, a venous thrombosis simulation and smart insole system are established to monitor venous blood clots and plantar pressures, respectively, which reveal potential applications in wearable medicine, sports health prediction, athlete training, and sports equipment design.

show abstract

“…With its good flexibility and stretchability, it can be attached to the surface of irregular objects, and it is widely used in the research of robotic bionic skin [ 6 , 7 , 8 , 9 ]. Therefore, researchers improve the performance of sensors from the aspects of materials [ 10 , 11 , 12 ], multifunctional integration [ 13 , 14 , 15 ], and self-energy [ 16 , 17 , 18 ]. At present, flexible tactile sensors based on capacitive [ 19 ], piezoresistive [ 20 , 21 ], piezoelectric [ 22 , 23 ], and other mechanisms have been continuously proposed, and they have shown good application prospects.…”

Section: Introductionmentioning

confidence: 99%

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

Yang

Wang

et al. 2022

Micromachines

View full text Add to dashboard Cite

Recognizing different contact patterns imposed on tactile sensors plays a very important role in human–machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) material. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN‑LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor.

show abstract

Direct stamping multifunctional tactile sensor for pressure and temperature sensing

Cited by 26 publications

References 50 publications

Recent Progress of Energy-Storage-Device-Integrated Sensing Systems

Recent Progress of Energy-Storage-Device-Integrated Sensing Systems

Capacitive Sensors with Hybrid Dielectric Structures and High Sensitivity over a Wide Pressure Range for Monitoring Biosignals

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

Contact Info

Product

Resources

About