The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Triboelectric Nanogenerators as a Self-Powered 3D Acceleration Sensor
Abstract
A novel self-powered acceleration sensor based on triboelectric nanogenerator is proposed, which consists of an outer transparent shell and an inner mass-spring-damper mechanical system. The PTFE films on the mass surfaces can slide between two aluminum electrodes on an inner wall owing to the acceleration in the axis direction. On the basis of the coupling of triboelectric and electrostatic effects, the potential difference between the two aluminum electrodes is generated in proportion to the mass displacement, which can be used to characterize the acceleration in the axis direction with a detection range from about 13.0 to 40.0 m/s(2) at a sensitivity of 0.289 V·s(2)/m. With the integration of acceleration sensors in three axes, a self-powered 3D acceleration sensor is developed for vector acceleration measurement in any direction. The self-powered 3D acceleration sensor has excellent performance in the stability test, and the output voltages have a little decrease of ∼6% after 4000 cycles. Moreover, the self-powered acceleration sensor can be used to measure high collision acceleration, which has potential practicability in automobile security systems.
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
Yang, Y., Zhang, H., Lin, Z.-H., Zhou, Y. S., Jing, Q., Su, Y., Yang, J., Chen, J., Hu, C., & Wang, Z. L. (2013) · ACS Nano · doi:10.1021/nn403838y · Cited by 798
Self-citation
Cited by
Recent progress of triboelectric nanogenerators: From fundamental theory to practical applications
Luo, J., & Wang, Z. L. (2020) · EcoMat · doi:10.1002/eom2.12059 · Cited by 451
Self-citationZhang, B., Zhang, L., Deng, W., Jin, L., Chun, F., Pan, H., Gu, B., Zhang, H., Lv, Z., Yang, W., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b03818 · Cited by 373
Self-citationControlling surface charge generated by contact electrification: Strategies and applications
Chen, L., Shi, Q., Sun, Y., Nguyen, T., Lee, C., & Soh, S. (2018) · Advanced Materials · doi:10.1002/adma.201802405 · Cited by 156
Huang, M., Zhu, M., Feng, X., Zhang, Z., Tang, T., Guo, X., Chen, T., Liu, H., Sun, L., & Lee, C. (2023) · ACS Nano · doi:10.1021/acsnano.2c11366 · Cited by 90
Metadata
- Authors
- Pang, Y. K., Li, X. H., Chen, M. X., Han, C. B., Zhang, C., & Wang, Z. L.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-08-18
- Volume
- 7
- Issue
- 34
- Pages
- 19076-19082
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2411484710
- Cited by
- 167
- References
- 1
- FWCI
- 7.23
- 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.
