Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 336

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Ultrastretchable and Stable Strain Sensors Based on Antifreezing and Self-Healing Ionic Organohydrogels for Human Motion Monitoring

Wu, Jin, Wu, Zixuan, Lu, Xing, Han, Songjia, Yang, Bo-Ru, Gui, Xuchun, Tao, Kai, Miao, Jianmin, Liu, Chuan·2019·ACS Applied Materials & Interfaces

Abstract

Ionic hydrogels, a class of intrinsically stretchable and conductive materials, are widely used in soft electronics. However, the easy freezing and drying of water-based hydrogels significantly limit their long-term stability. Here, a facile solvent-replacement strategy is developed to fabricate ethylene glycol (Eg)/glycerol (Gl)-water binary antifreezing and antidrying organohydrogels for ultrastretchable and sensitive strain sensing within a wide temperature range. Because of the ready formation of strong hydrogen bonds between Eg/Gl and water molecules, the organohydrogels gain exceptional freezing and drying tolerance with retained deformability, conductivity, and self-healing ability even stay at extreme temperature for a long time. Thus, the fabricated strain sensor displays a gauge factor of 6, which is much higher than previously reported values for hydrogel-based strain sensors. Furthermore, the strain sensor exhibits a relatively wide strain range (0.5-950%) even at -18 °C. Various human motions with different strain levels are monitored by the strain sensor with good stability and repeatability from -18 to 25 °C. The organohydrogels maintained the strain sensing capability when exposed to ambient air for nine months. This work provides new insight into the fabrication of stable, ultrastretchable, and ultrasensitive strain sensors using chemically modified organohydrogel for emerging wearable electronics.

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/acsami.8b20267

Cites

56 of 56 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

Cited by

See all 336

Metadata

Authors
Wu, J., Wu, Z., Lu, X., Han, S., Yang, B.-R., Gui, X., Tao, K., Miao, J., & Liu, C.
Venue
ACS Applied Materials & Interfaces
Published
2019-02-14
Volume
11
Issue
9
Pages
9405-9414
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W2912435822
Cited by
336
References
56
FWCI
18.49
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

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.