Evidence map›Paper›PMID 41569615›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Understanding the Roles of Microstructure and Viscoelasticity of Soft Ionic Elastomer for Super-Capacitive Pressure Sensors.

Allen J Cheng, Wenkai Chang, Zhuohan Cao, Zhao Sha, Shuai He, Ming Xuan Chua, Bingnong Jiang, Yuansen Qiao, Ziyan Gao, Wenkui Dong and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Allen J ChengSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-1895-0405
Wenkai ChangSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Zhuohan CaoSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Zhao ShaSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Shuai HeSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Ming Xuan ChuaSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Bingnong JiangSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Yuansen QiaoSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Ziyan GaoSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Wenkui DongCentre For Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales, Australia.
Wengui LiCentre For Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, New South Wales, Australia.
Liao WuSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Dewei ChuSchool of Materials Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Shuhua PengSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-5680-9448

Funding

Australian Research Council DP210100879Australian Research Council DP250100714Australian Research Council FT220100094Australian Research Council IH210100040
6 · The paper itself

Abstract

Soft ionic conductive elastomers offer unique advantages for super-capacitive pressure sensors, where the electrical double layer (EDL) effect enables high sensitivity and rapid response. However, the roles of microstructure and viscoelasticity on EDL-driven sensing remain poorly understood. This study establishes detailed correlations between elastomer microstructure, intrinsic viscoelastic properties, and sensor performance by integrating mechanical and electrical analyses. Validation of the EDL mechanism reveals how microstructural optimization and viscoelastic tuning enhance sensitivity, linear range, and stability. Height-graded architectures yield a sensor with a sensitivity of 2.70 nF/kPa, a broad linear range of 0-2000 kPa, and robust durability over 10 000 cycles. These devices demonstrate multifunctionality in robotic electronic skin, pressure mapping, and real-time physiological monitoring such as wrist pulse detection. The findings establish key structure-property-performance relationships, providing design guidelines for next-generation, high-performance super-capacitive sensors.

Indexed as

linear rangemicrostructure optimizationsensitivitysuper‐capacitive pressure sensorviscoelasticity

Identifiers

PMID41569615
PMCPMC13042398

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.