Ultra‐Sensitive and Linear Flexible Pressure Sensors with Tri‐Scale Graded Microstructures for Advanced Health Monitoring and Robotic Perception

Flexible piezoresistive sensors, which combine high sensitivity and a wide linear detection range, are ideal choices for human health monitoring and robotic perception. However, sensors often exhibit a trade-off between sensitivity and linearity, with challenges caused by the incompressibility of soft materials and the stiffening of microstructures. In this study, a flexible pressure sensor with a 3D ordered tri-scale graded microstructure, fabricated by laser processing, is proposed. The sensor achieves an ultra-high sensitivity of 138.6 kPa<sup>-1</sup> and a linear range up to 400 kPa (R<sup>2</sup> = 0.99). The compensation behavior derived from the tri-scale graded microstructure's compression deformation counteracts contact hardening and delays sensitivity saturation. Furthermore, the sensor demonstrates a minimum detectable limit as low as 3 Pa, with response and recovery times of 34/39 ms, showing excellent stability after over 24 000 repeated loading cycles. Physiological monitoring confirms that the sensor can accurately capture a wide range of pressure-variations, including those from the carotid artery, jugular vein, respiration, throat vibrations, and foot pressure. Additionally, the sensor can be used for remote operation of robotic hands. This work provides a strategy for manufacturing flexible pressure sensors with a combination of high sensitivity, high linearity, and a wide pressure response range.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2025-10-20
DOI
https://doi.org/10.1002/advs.202516810
Citations
4
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
2.70

Funders

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

Ultra‐Sensitive and Linear Flexible Pressure Sensors with Tri‐Scale Graded Microstructures for Advanced Health Monitoring and Robotic Perception

Wei Zhou, Chunjin Wang, Rui Gao, Rui Chen et al.
4 citations
Advanced Science
Advanced Sensor and Energy Harvesting Materials
2.70
article

Ultra‐Sensitive and Linear Flexible Pressure Sensors with Tri‐Scale Graded Microstructures for Advanced Health Monitoring and Robotic Perception

Wei Zhou, Chunjin Wang, Rui Gao, Rui Chen, Qixian Zhang, Tao Luo
article en
4 citations

Abstract

Flexible piezoresistive sensors, which combine high sensitivity and a wide linear detection range, are ideal choices for human health monitoring and robotic perception. However, sensors often exhibit a trade-off between sensitivity and linearity, with challenges caused by the incompressibility of soft materials and the stiffening of microstructures. In this study, a flexible pressure sensor with a 3D ordered tri-scale graded microstructure, fabricated by laser processing, is proposed. The sensor achieves an ultra-high sensitivity of 138.6 kPa<sup>-1</sup> and a linear range up to 400 kPa (R<sup>2</sup> = 0.99). The compensation behavior derived from the tri-scale graded microstructure's compression deformation counteracts contact hardening and delays sensitivity saturation. Furthermore, the sensor demonstrates a minimum detectable limit as low as 3 Pa, with response and recovery times of 34/39 ms, showing excellent stability after over 24 000 repeated loading cycles. Physiological monitoring confirms that the sensor can accurately capture a wide range of pressure-variations, including those from the carotid artery, jugular vein, respiration, throat vibrations, and foot pressure. Additionally, the sensor can be used for remote operation of robotic hands. This work provides a strategy for manufacturing flexible pressure sensors with a combination of high sensitivity, high linearity, and a wide pressure response range.

Advanced ScienceVol. 13(1)
Hong Kong Polytechnic University (HK), Xiamen University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Advanced Sensor and Energy Harvesting Materials
2.70
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.