The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Magnetic-Assisted, Self-Healable, Yarn-Based Supercapacitor
Abstract
Yarn-based supercapacitors have received considerable attention recently, offering unprecedented opportunities for future wearable electronic devices (e.g., smart clothes). However, the reliability and lifespan of yarn-based supercapacitors can be seriously limited by accidental mechanical damage during practical applications. Therefore, a supercapacitor endowed with mechanically and electrically self-healing properties is a brilliant solution to the challenge. Compared with the conventional planar-like or large wire-like structure, the reconnection of the broken yarn electrode composed of multiple tiny fibers (diameter <20 μm) is much more difficult and challenging, which directly affects the restoration of electrical conductivity after damage. Herein, a self-healable yarn-based supercapacitor that ensures the reconnection of broken electrodes has been successfully developed by wrapping magnetic electrodes around a self-healing polymer shell. The strong force from magnetic attraction between the broken yarn electrodes benefits reconnection of fibers in the yarn electrodes during self-healing and thus offers an effective strategy for the restoration of electric conductivity, whereas the polymer shell recovers the configuration integrity and mechanical strength. With the design, the specific capacitance of our prototype can be restored up to 71.8% even after four breaking/healing cycles with great maintenance of the whole device's mechanical properties. This work may inspire the design and fabrication of other distinctive self-healable and wearable electronic devices.
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
Cites
Poly(vinyl alcohol) Hydrogel Can Autonomously Self-Heal
Zhang, H., Xia, H., & Zhao, Y. (2012) · ACS Macro Letters · doi:10.1021/mz300451r · Cited by 509
Cited by
Gel Polymer Electrolytes for Electrochemical Energy Storage
Cheng, X., Pan, J., Zhao, Y., Liao, M., & Peng, H. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201702184 · Cited by 1,020
Yetisen, A. K., Qu, H., Manbachi, A., Butt, H., Dokmeci, M. R., Hinestroza, J. P., Skorobogatiy, M., Khademhosseini, A., & Yun, S. H. (2016) · ACS Nano · doi:10.1021/acsnano.5b08176 · Cited by 752
Highly Self-Healable 3D Microsupercapacitor with MXene–Graphene Composite Aerogel
Yue, Y., Liu, N., Ma, Y., Wang, S., Liu, W., Luo, C., Zhang, H., Cheng, F., Rao, J., Hu, X., et al. (2018) · ACS Nano · doi:10.1021/acsnano.7b07528 · Cited by 736
Kang, J., Tok, J. B.-H., & Bao, Z. (2019) · Nature Electronics · doi:10.1038/s41928-019-0235-0 · Cited by 706
Multifunctional Energy Storage and Conversion Devices
Huang, Y., Zhu, M., Huang, Y., Pei, Z., Li, H., Wang, Z., Xue, Q., & Zhi, C. (2016) · Advanced Materials · doi:10.1002/adma.201601928 · Cited by 544
Self-citation
Metadata
- Authors
- Huang, Y., Huang, Y., Zhu, M., Meng, W., Pei, Z., Liu, C., Hu, H., & Zhi, C.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-06-04
- Volume
- 9
- Issue
- 6
- Pages
- 6242-6251
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2415203182
- Cited by
- 371
- References
- 1
- FWCI
- 13.67
- 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: Magnetic-Assisted, Self-Healable, Yarn-Based Supercapacitor. https://app.cochat.ai/papers/10.1021/acsnano.5b01602
- The paper
- Huang, Y., Huang, Y., Zhu, M., Meng, W., Pei, Z., Liu, C., Hu, H., & Zhi, C. (2015). Magnetic-Assisted, Self-Healable, Yarn-Based Supercapacitor. ACS Nano. https://doi.org/10.1021/acsnano.5b01602
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.