Hofmeister Hydrogel Iontronic Sensor for Dual-Mode Motion Monitoring

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 <i>Hofmeister effect</i>. By introducing weakly hydrated I<sup>-</sup> and strongly hydrated SO<sub>4</sub><sup>2<sup>-</sup></sup>, 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<sup>-1</sup>, 0-50 kPa), ideal for subtle signals such as pulse and vocalization, while the hard mode maintains stable linearity (1.5 kPa<sup>-1</sup>, 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 <i>Hofmeister effect</i>-regulated hydrogel design for programmable modulus control, enabling high-performance signal recognition and advancing flexible iontronic sensors for intelligent health monitoring.

Authors

Institutions

Publication Details

Journal
ACS Sensors
Published
2026-02-26
DOI
https://doi.org/10.1021/acssensors.5c03822
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Hofmeister Hydrogel Iontronic Sensor for Dual-Mode Motion Monitoring

Jintao Yuan, Zhongbin Wu, Tianci Xu, Jinquan Li et al.
ACS Sensors
Advanced Sensor and Energy Harvesting Materials
article

Hofmeister Hydrogel Iontronic Sensor for Dual-Mode Motion Monitoring

Jintao Yuan, Zhongbin Wu, Tianci Xu, Jinquan Li, Ying Lü, Tao Chen
article en

Abstract

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 <i>Hofmeister effect</i>. By introducing weakly hydrated I<sup>-</sup> and strongly hydrated SO<sub>4</sub><sup>2<sup>-</sup></sup>, 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<sup>-1</sup>, 0-50 kPa), ideal for subtle signals such as pulse and vocalization, while the hard mode maintains stable linearity (1.5 kPa<sup>-1</sup>, 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 <i>Hofmeister effect</i>-regulated hydrogel design for programmable modulus control, enabling high-performance signal recognition and advancing flexible iontronic sensors for intelligent health monitoring.

ACS SensorsVol. 11(3)
Anhui Agricultural University (CN), Lingnan Normal University (CN), Hungarian University of Sports Science (HU)
Reduced inequalities
Openalex Percentile: Top 43%
Advanced Sensor and Energy Harvesting Materials
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.