Evidence map›Paper›PMID 42535784›Full record

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

Data-Driven Design of Self-Adhesive Epidermal Electrodes and Sensors.

Xuan Li, Shilei Wang, Milad Razbin, Danish Tahir, Chen Sang, Shuhua Peng, Markus Müllner, Wenlong Cheng, Wei Chen, Chun Hui Wang and 1 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

11 authors.

Xuan LiSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Shilei WangSchool of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Milad RazbinSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Danish TahirSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Chen SangSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Shuhua PengSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia.
Markus MüllnerKey Centre for Polymers & Colloids, School of Chemistry, The University of Sydney, Sydney, New South Wales, Australia.
Wenlong ChengSchool of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Wei ChenSchool of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Chun Hui WangSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia.
Shuying WuSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-6585-8898

Funding

ARC Discovery Project DP240101993ARC Discovery Project DP250100714ARC for a Future Fellowship FT200100185ARC Future Fellowship FT220100094ARC Industrial Transformation Research Hubs IH210100040ARC Linkage Project LP240200715Discovery Project DP220100452Global Science and Technology Diplomacy Fund GSTDS000001 - 6
6 · The paper itself

Abstract

Stretchable and self-adhesive epidermal electrodes and sensors with long-term stability are highly desirable for wearable applications. These electrodes and sensors typically comprise multiple components, each contributing distinct mechanical and electrical functions. However, their materials design and optimization rely heavily on time-consuming trial-and-error approaches, highlighting the need for a more effective strategy. Here, we report a data-driven composition optimization strategy integrating artificial neural network (ANN) modeling and genetic algorithm (GA) optimization for the rational design of self-adhesive epidermal electrodes/sensors. By defining optimization objectives that prioritized either high electrical conductivity and adhesion or high piezoresistive sensitivity, stretchable epidermal electrodes and sensors were developed. The optimized electrode exhibits high stretchability (∼ 177%), robust adhesion (0.10 N cm

Indexed as

biopotentialdata‐drivenepidermal electrodesself‐adhesive

Identifiers

PMID42535784
PMCPMC13426098

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.