The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics
Open accessAbstract
In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.
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
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630
Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors
Wang, Z. L., Chen, J., & Lin, L. (2015) · Energy & Environmental Science · doi:10.1039/c5ee01532d · Cited by 2,174
Self-citationAn IoT-Aware Architecture for Smart Healthcare Systems
Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2417684 · Cited by 1,167
Park, S., Kim, H., Vosgueritchian, M., Cheon, S., Kim, H., Koo, J. H., Kim, T. R., Lee, S., Schwartz, G., Chang, H., et al. (2014) · Advanced Materials · doi:10.1002/adma.201402574 · Cited by 565
Cited by
Material choices for triboelectric nanogenerators: A critical review
Zhang, R., & Olin, H. (2020) · EcoMat · doi:10.1002/eom2.12062 · Cited by 533
Sun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., et al. (2024) · Nano-Micro Letters · doi:10.1007/s40820-023-01235-x · Cited by 384
He, W., Liu, W., Chen, J., Wang, Z., Liu, Y., Pu, X., Yang, H., Tang, Q., Yang, H., Guo, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18086-4 · Cited by 303
Self-citationWood-Based Flexible Electronics
Fu, Q., Chen, Y., & Sorieul, M. (2020) · ACS Nano · doi:10.1021/acsnano.9b09817 · Cited by 269
Pang, Y., He, T., Liu, S., Zhu, X., & Lee, C. (2024) · Advanced Science · doi:10.1002/advs.202306574 · Cited by 60
Metadata
- Authors
- Luo, J., Wang, Z., Xu, L., Wang, A. C., Han, K., Jiang, T., Lai, Q., Bai, Y., Tang, W., Fan, F. R., & Wang, Z. L.
- Venue
- Nature Communications
- Published
- 2019-11-26
- Volume
- 10
- Issue
- 1
- Pages
- 5147-5147
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2991376568
- Cited by
- 542
- References
- 4
- FWCI
- 19.49
- Type
- journal article
- Language
- en
- Open access
- gold
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: Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics. https://app.cochat.ai/papers/10.1038/s41467-019-13166-6
- The paper
- Luo, J., Wang, Z., Xu, L., Wang, A. C., Han, K., Jiang, T., Lai, Q., Bai, Y., Tang, W., Fan, F. R., & Wang, Z. L. (2019). Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics. Nature Communications. https://doi.org/10.1038/s41467-019-13166-6
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.