The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
An impedance tunable and highly efficient triboelectric nanogenerator for large-scale, ultra-sensitive pressure sensing applications
Abstract
Abstract Precise triboelectric nanogenerators (TENGs) with large-scale pressure sensing ability can be realized by effectively harvesting physical pressure. Extensive research on efficient pressure sensors is ongoing, yet the pressure detection limit and sensitivity of most of the reported pressure sensors are not satisfactory for practical and wearable device applications. Herein, we demonstrate a highly efficient approach toward detecting a wide range of pressures, from 5 kPa to 450 kPa, with a record high sensitivity of 0.51 V/kPa. We aim at maximizing the energy conversion efficiency of 48.17% by optimally tuning the internal impedance of the triboelectric nanogenerator at 2.5 MΩ, because low internal impedance results in high output power. This paper reports the structural design, fabrication, and experimental validation of a self-powered and highly durable TENG pressure sensor for large-scale pressure detection based on double-side tribological layers of micro-patterned polydimethylsiloxane (PDMS) and PDMS-multiwall carbon nanotube (CNT) nanocomposites. An in-sole application of the proposed TENG is demonstrated for varying foot pressures corresponding to different walking patterns, which is likely to be applicable in sports sciences, high-risk diabetic foot ulceration, and rehabilitation. Our present contribution not only facilitates large-scale pressure sensing but also paves the way toward the realization of next-generation self-powered and maintenance-free sensing 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
Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers
Mannsfeld, S. C. B., Tee, B. C.-K., Stoltenberg, R. M., Chen, C. V. H.-H., Barman, S., Muir, B. V. O., Sokolov, A. N., Reese, C., & Bao, Z. (2010) · Nature Materials · doi:10.1038/nmat2834 · Cited by 3,291
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
Schwartz, G., Tee, B. C.-K., Mei, J., Appleton, A. L., Kim, D. H., Wang, H., & Bao, Z. (2013) · Nature Communications · doi:10.1038/ncomms2832 · Cited by 2,042
Advances of flexible pressure sensors toward artificial intelligence and health care applications
Zang, Y., Zhang, F., Di, C.-A., & Zhu, D. (2015) · Materials Horizons · doi:10.1039/c4mh00147h · Cited by 1,272
Wu, W., Wen, X., & Wang, Z. L. (2013) · Science · doi:10.1126/science.1234855 · Cited by 931
Park, J., Lee, Y., Hong, J., Ha, M., Jung, Y.-D., Lim, H., Kim, S. Y., & Ko, H. (2014) · ACS Nano · doi:10.1021/nn500441k · Cited by 915
Self-citationYang, 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
Xie, Y., Wang, S., Niu, S., Lin, L., Jing, Q., Yang, J., Wu, Z., & Wang, Z. L. (2014) · Advanced Materials · doi:10.1002/adma.201402428 · Cited by 554
Bai, P., Zhu, G., Jing, Q., Yang, J., Chen, J., Su, Y., Ma, J., Zhang, G., & Wang, Z. L. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201401267 · Cited by 282
Cited by
Choi, D., Lee, Y., Lin, Z.-H., Cho, S., Kim, M., Ao, C. K., Soh, S., Sohn, C., Jeong, C. K., Lee, J., et al. (2023) · ACS Nano · doi:10.1021/acsnano.2c12458 · Cited by 656
Self-citationShi, Z., Meng, L., Shi, X., Li, H., Zhang, J., Sun, Q., Liu, X., Chen, J., & Liu, S. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00874-w · Cited by 282
Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges
Homayounfar, S. Z., & Andrew, T. L. (2020) · SLAS TECHNOLOGY · doi:10.1177/2472630319891128 · Cited by 227
Metadata
- Authors
- Rasel, M. S., Maharjan, P., Salauddin, M., Rahman, M. T., Cho, H. O., Kim, J. W., & Park, J. Y.
- Venue
- Nano Energy
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 49
- Pages
- 603-613
- ISSN
- 2211-2855 (ISSN-L)
- OpenAlex
- W2800210393
- Cited by
- 173
- References
- 9
- FWCI
- 7.08
- 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: An impedance tunable and highly efficient triboelectric nanogenerator for large-scale, ultra-sensitive pressure sensing applications. https://app.cochat.ai/papers/10.1016/j.nanoen.2018.04.060
- The paper
- Rasel, M. S., Maharjan, P., Salauddin, M., Rahman, M. T., Cho, H. O., Kim, J. W., & Park, J. Y. (2018). An impedance tunable and highly efficient triboelectric nanogenerator for large-scale, ultra-sensitive pressure sensing applications. Nano Energy. https://doi.org/10.1016/j.nanoen.2018.04.060
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.