Ionic conductivity and excitation frequency effects in iontronic pressure sensing

Iontronic pressure sensors, which combine high sensitivity, superior signal stability, and resistance to electromagnetic interference, are regarded as promising candidates for next-generation pressure sensing systems. However, the roles of ionic conductivity and excitation frequency in determining sensing behavior remains unknown. Here, we employ an ionic-droplet-based research model to elucidate these effects through electrochemical impedance spectroscopy (EIS). Based on the equivalent circuit, we derived equations that link apparent capacitance change to ionic conductivity and excitation frequency. The equations reveal that droplet compression leads to larger apparent capacitance variation at lower ionic conductivity and higher excitation frequency, consistent with our experimental observations. Accordingly, tuning these parameters alone enhances the sensitivity of the droplet-based pressure sensor by up to 8,772% without any structural modification. Under the optimized conditions, the sensor delivers highly repeatable pressure responses. Integrated into a robotic arm, it enables real-time pressure detection and discrimination of materials with different softness levels. By substituting ionic-droplets with hydrogels, the sensing model is successfully extended to solid-state ionic materials. A 4 × 4 hydrogel sensor array with 2 mm pixel size is fabricated, enabling spatial pressure mapping. These findings provide new design principles for elastic ionic materials and underscore the critical role of measurement parameters in determining the sensing behavior of ionic sensing systems.

Authors

Publication Details

Journal
Nano Research
Published
2026-04-01
DOI
https://doi.org/10.26599/nr.2026.94908691
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

Ionic conductivity and excitation frequency effects in iontronic pressure sensing

Jin Ge, Junshuai Chen, Jiasen Xie, Yongming Wang et al.
Nano Research
Advanced Sensor and Energy Harvesting Materials
article

Ionic conductivity and excitation frequency effects in iontronic pressure sensing

Jin Ge, Junshuai Chen, Jiasen Xie, Yongming Wang, Jinhui Gu, Xingyu Ma, Yunjie Lu, Lin Zheng, Mingwei Gu, Ying Yang
article en

Abstract

Iontronic pressure sensors, which combine high sensitivity, superior signal stability, and resistance to electromagnetic interference, are regarded as promising candidates for next-generation pressure sensing systems. However, the roles of ionic conductivity and excitation frequency in determining sensing behavior remains unknown. Here, we employ an ionic-droplet-based research model to elucidate these effects through electrochemical impedance spectroscopy (EIS). Based on the equivalent circuit, we derived equations that link apparent capacitance change to ionic conductivity and excitation frequency. The equations reveal that droplet compression leads to larger apparent capacitance variation at lower ionic conductivity and higher excitation frequency, consistent with our experimental observations. Accordingly, tuning these parameters alone enhances the sensitivity of the droplet-based pressure sensor by up to 8,772% without any structural modification. Under the optimized conditions, the sensor delivers highly repeatable pressure responses. Integrated into a robotic arm, it enables real-time pressure detection and discrimination of materials with different softness levels. By substituting ionic-droplets with hydrogels, the sensing model is successfully extended to solid-state ionic materials. A 4 × 4 hydrogel sensor array with 2 mm pixel size is fabricated, enabling spatial pressure mapping. These findings provide new design principles for elastic ionic materials and underscore the critical role of measurement parameters in determining the sensing behavior of ionic sensing systems.

Nano Research
Peace, Justice and strong institutions
Openalex Percentile: Top 6%
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.