The DOI matches its Crossref record.
Mechanoresponsive Healable Metallosupramolecular Polymers
Abstract
The development of polymers that possess superb mechanical properties and at the same time are capable of sensing damage and self-healing is presented. Copper-catalyzed azide–alkyne cycloaddition (CuAAC) based tridentate ligand 2,6-bis(1,2,3-triazol-4-yl)pyridine (BTP) and covalent mechanophore spiropyran (SP) units are incorporated into the polymer backbone to prepare ligand macromolecule. Upon coordinating with transition or lanthanide metal salts, metallosupramolecular films with phased-separated soft/hard morphology are spontaneously formed. The resulting materials show a rare combination of strong, tough, and elastic mechanical properties and are able to sense damage by changing optical properties. The Zn 2+ -containing material can self-heal in the presence of solvent and fully restore its mechanical properties. The underlying structure–property relationship is unveiled. In particular, the interplay between the covalent SP mechanophore and the noncovalent metal–ligand interactions and their hard phase is demonstrated.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Zhang, Q., Niu, S., Wang, L., Lopez, J., Chen, S., Cai, Y., Du, R., Liu, Y., Lai, J., Liu, L., et al. (2018) · Advanced Materials · doi:10.1002/adma.201801435 · Cited by 398
Zhu, X., Zhang, W., Lu, G., Zhao, H., & Wang, L. (2022) · ACS Nano · doi:10.1021/acsnano.2c06264 · Cited by 279
Ezeigwe, E. R., Dong, L., Manjunatha, R., Tan, M., Yan, W., & Zhang, J. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.105907 · Cited by 87
Metadata
- Authors
- Hong, G., Zhang, H., Lin, Y., Chen, Y., Xu, Y., Weng, W., & Xia, H.
- Venue
- Macromolecules
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-10-16
- Volume
- 46
- Issue
- 21
- Pages
- 8649-8656
- ISSN
- 0024-9297 (ISSN-L)
- 1520-5835
- OpenAlex
- W2082573532
- Cited by
- 167
- References
- 0
- FWCI
- 12.21
- 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: Mechanoresponsive Healable Metallosupramolecular Polymers. https://app.cochat.ai/papers/10.1021/ma4017532
- The paper
- Hong, G., Zhang, H., Lin, Y., Chen, Y., Xu, Y., Weng, W., & Xia, H. (2013). Mechanoresponsive Healable Metallosupramolecular Polymers. Macromolecules. https://doi.org/10.1021/ma4017532
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.