Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 500

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Multiple Hydrogen Bonding Enables the Self‐Healing of Sensors for Human–Machine Interactions

Cao, Jie, Lu, Canhui, Zhuang, Jian, Liu, Manxiao, Zhang, Xinxing, Yu, Yanmei, Tao, Qingchuan·2017·Angewandte Chemie International Edition

Abstract

Despite its widespread use in signal collection, flexible sensors have been rarely used in human–machine interactions owing to its indistinguishable signal, poor reliability, and poor stability when inflicted with unavoidable scratches and/or mechanical cuts. A highly sensitive and self‐healing sensor enabled by multiple hydrogen bonding network and nanostructured conductive network is demonstrated. The nanostructured supramolecular sensor displays extremely fast (ca. 15 s) and repeatable self‐healing ability with high healing efficiency (93 % after the third healing process). It can precisely detect tiny human motions, demonstrating highly distinguishable and reliable signals even after cutting–healing and bending over 20 000 cycles. Furthermore, a human–machine interaction system is integrated to develop a facial expression control system and an electronic larynx, aiming to control the robot to assist the patient's daily life and help the mute to realize real‐time speaking.

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 · 10.1002/anie.201704217

Cites

1 of 1 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 1 references

Cited by

See all 500

Metadata

Authors
Cao, J., Lu, C., Zhuang, J., Liu, M., Zhang, X., Yu, Y., & Tao, Q.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2017-06-14
Volume
56
Issue
30
Pages
8795-8800
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2618173545
Cited by
500
References
1
FWCI
21.88
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: Multiple Hydrogen Bonding Enables the Self‐Healing of Sensors for Human–Machine Interactions. https://app.cochat.ai/papers/10.1002/anie.201704217
The paper
Cao, J., Lu, C., Zhuang, J., Liu, M., Zhang, X., Yu, Y., & Tao, Q. (2017). Multiple Hydrogen Bonding Enables the Self‐Healing of Sensors for Human–Machine Interactions. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201704217

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.