CNTs@fabric/Ni@PU piezoresistive sensor with enhanced interfacial contact resistance variation for motion detection and deep-learning-assisted recognition

Abstract Flexible piezoresistive sensors based on the mechanism of interfacial contact resistance change are receiving increasing attention in the fields of human-computer interaction, health monitoring, and behavior tracking. However, the high cost and complex manufacturing process limit the wide application and development of these flexible piezoresistive sensors. Here, a novel carbon nanotubes@fabric (CNTs@fabric)/Ni@polyurethane (Ni@PU) piezoresistive sensor (CNPS) with low-cost, simple-preparation and high-sensitivity was proposed. The effective contact area is obtained by synergizing the woven micro-convex structure of the fabric, the large specific surface area of the CNTs and the porous three dimensional electrodes. Within the small pressure (0–9.52 kPa) effect, the area of connection with the electrodes to the active layer plays a dominant role, resulting in a sensitivity of up to 6.39 kPa −1 for CNPS. In the high pressure region (9.52–44.92 kPa), where internal mechanism of change in the sensitive material dominants, the CNPS has a response time of 85 ms at a constant pressure of 28.31 kPa. Considering the excellent output electrical performances, a variety of body movements could be detected by fixing the CNPS to different joints. Significantly, the designed intelligent object recognition system implemented by the combination of matrix stress detection module and residual neural network (ResNet) algorithm has a recognition accuracy of 99.26%. Enhancing the interfacial contact resistance change mechanism using a simple fabrication process offers a promising strategy for the rapid development of flexible piezoresistive sensors.

Authors

Publication Details

Journal
Smart Materials and Structures
Published
2024-10-11
DOI
https://doi.org/10.1088/1361-665x/ad860f
Citations
3
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CNTs@fabric/Ni@PU piezoresistive sensor with enhanced interfacial contact resistance variation for motion detection and deep-learning-assisted recognition

Jie Zhu, Xin Luo, Shuai Zhang, Shuqi Ma et al.
3 citations
Smart Materials and Structures
Advanced Sensor and Energy Harvesting Materials
0.55
article

CNTs@fabric/Ni@PU piezoresistive sensor with enhanced interfacial contact resistance variation for motion detection and deep-learning-assisted recognition

Jie Zhu, Xin Luo, Shuai Zhang, Shuqi Ma, Jinchen Liu, Jiacheng Wang, Hancheng Chai
article en
3 citations

Abstract

Abstract Flexible piezoresistive sensors based on the mechanism of interfacial contact resistance change are receiving increasing attention in the fields of human-computer interaction, health monitoring, and behavior tracking. However, the high cost and complex manufacturing process limit the wide application and development of these flexible piezoresistive sensors. Here, a novel carbon nanotubes@fabric (CNTs@fabric)/Ni@polyurethane (Ni@PU) piezoresistive sensor (CNPS) with low-cost, simple-preparation and high-sensitivity was proposed. The effective contact area is obtained by synergizing the woven micro-convex structure of the fabric, the large specific surface area of the CNTs and the porous three dimensional electrodes. Within the small pressure (0–9.52 kPa) effect, the area of connection with the electrodes to the active layer plays a dominant role, resulting in a sensitivity of up to 6.39 kPa −1 for CNPS. In the high pressure region (9.52–44.92 kPa), where internal mechanism of change in the sensitive material dominants, the CNPS has a response time of 85 ms at a constant pressure of 28.31 kPa. Considering the excellent output electrical performances, a variety of body movements could be detected by fixing the CNPS to different joints. Significantly, the designed intelligent object recognition system implemented by the combination of matrix stress detection module and residual neural network (ResNet) algorithm has a recognition accuracy of 99.26%. Enhancing the interfacial contact resistance change mechanism using a simple fabrication process offers a promising strategy for the rapid development of flexible piezoresistive sensors.

Smart Materials and StructuresVol. 33(11)
National Natural Science Foundation of China, Natural Science Foundation of Hebei Province
Openalex Percentile: Top 37%
Advanced Sensor and Energy Harvesting Materials
0.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.