信号(编程语言)
计算机科学
线性
传感器
灵敏度(控制系统)
人工神经网络
材料科学
自愈水凝胶
联轴节(管道)
聚乙烯醇
人工智能
人体运动
数据采集
压力传感器
软传感器
脉搏(音乐)
灵活性(工程)
信号处理
判别式
纳米技术
工作(物理)
生物系统
特征(语言学)
声学
模式识别(心理学)
作者
Tao Chen,Jinquan Li,Tianci Xu,Jintao Yuan,Zhongbin Wu,Ying Lu
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2026-02-26
卷期号:11 (3): 2121-2129
标识
DOI:10.1021/acssensors.5c03822
摘要
Flexible pressure sensors hold great promise for applications in motion monitoring and human signal recognition. However, conventional devices with fixed structure-performance coupling often fail to meet the diverse demands of different body sites and multi-scenario monitoring. Herein, we propose a polyvinyl alcohol (PVA)-hydroxyethane diphosphonic acid (HEDP) hydrogel iontronic sensor (PHHIS) with modulus programmability regulated by the Hofmeister effect. By introducing weakly hydrated I- and strongly hydrated SO42-, we constructed soft-mode and hard-mode PHHIS, enabling precise acquisition of both low- and high-pressure human signals. The soft mode achieves ultrahigh sensitivity (238.4 kPa-1, 0-50 kPa), ideal for subtle signals such as pulse and vocalization, while the hard mode maintains stable linearity (1.5 kPa-1, 0-800 kPa), suitable for limb movement monitoring. Eight classes of human signals were precisely recognized with 100% accuracy through feature extraction combined with linear discriminant analysis, hierarchical cluster analysis, and artificial neural network analysis. This work demonstrates the promise of Hofmeister effect-regulated hydrogel design for programmable modulus control, enabling high-performance signal recognition and advancing flexible iontronic sensors for intelligent health monitoring.
科研通智能强力驱动
Strongly Powered by AbleSci AI