Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 192

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Self-Healable Gels for Use in Wearable Devices

Li, Jingjing, Geng, Lifang, Wang, Gang, Chu, Huijuan, Wei, Hongliang·2017·Chemistry of Materials

Abstract

Self-healing gels, which are able to restore their original shape/condition after damage, have recently attracted considerable interest. The present review provides an update on the current status of self-healing gels for use in soft self-healing devices, with the main focus on wearable devices. Following a brief introduction of conventional self-healing gels, this review summarizes the current strategies for synthesizing self-healing gels, comparing the properties of polymeric and small molecular self-healing gels. Some of the strengths and weaknesses of these two types of self-healing gels are clearly highlighted. Several typical instances to outline the emerging applications of self-healing gels with partially or fully self-healing devices/materials, including wearable sensors, supercapacitors, lithium batteries, and coatings, are provided. Finally, the current challenges and future perspectives regarding the further development of self-healing gels are also discussed.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acs.chemmater.7b02895

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Cited by

See all 192

Metadata

Authors
Li, J., Geng, L., Wang, G., Chu, H., & Wei, H.
Venue
Chemistry of Materials
Published
2017-10-19
Volume
29
Issue
21
Pages
8932-8952
ISSN
  • 0897-4756 (ISSN-L)
  • 1520-5002
OpenAlex
W2764087978
Cited by
192
References
5
FWCI
14.05
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Self-Healable Gels for Use in Wearable Devices. https://app.cochat.ai/papers/10.1021/acs.chemmater.7b02895
The paper
Li, J., Geng, L., Wang, G., Chu, H., & Wei, H. (2017). Self-Healable Gels for Use in Wearable Devices. Chemistry of Materials. https://doi.org/10.1021/acs.chemmater.7b02895

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.