Evidence map›Paper›PMID 41554732›Full record

ArticleNature communications2026

Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow.

Haochen Yang, Qiongyu Chen, Tianle Chen, Yang Li, Elizabeth A Norris, Joshua M Little, Jiayue Sun, Snehi Shrestha, Edison Chen, Shenqiang Ren and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Haochen YangDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0002-8367-9711
Qiongyu ChenDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0001-5283-5614
Tianle ChenDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0003-1332-0399
Yang LiDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0001-7816-9445
Elizabeth A NorrisDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0003-0084-1670
Joshua M LittleDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.
Jiayue SunDepartment of Chemistry, University of Maryland, College Park, MD, USA.
Snehi ShresthaDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.
Edison ChenDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA.
Shenqiang RenDepartment of Materials Science and Engineering, University of Maryland, College Park, MD, USA.ORCID http://orcid.org/0000-0002-9987-3316
Teng LiDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA. lit@umd.edu.ORCID http://orcid.org/0000-0001-6252-561X
Po-Yen ChenDepartment of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA. checp@umd.edu.ORCID http://orcid.org/0000-0003-0310-4748

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Conventional approaches, such as design of experiments and trial-and-error methods, often rely on time-consuming and labor-intensive experiments to navigate a vast and complex parameter space. To overcome this, we establish an integrated workflow merging robot-automated experimentation, machine learning predictions, and finite element simulations to enable the predictive design of stretchable electrodes with strain-insensitive performance. Initially, we construct an ensemble of artificial neural networks through a two-stage workflow, including feasible parameter space definition and active learning loops. Leveraging the prediction model and two-scale simulations, a microtextured stretchable nanocomposite is discovered as a strain-stable platform. Conformal deposition of a thin gold layer showcases metal-like conductivity, high resistance-insensitive stretchability, and robust durability. Furthermore, electrodeposition of Zn and MnO

Identifiers

PMID41554732
PMCPMC12916966

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.