The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Graphene-Paper Pressure Sensor for Detecting Human Motions
Abstract
Pressure sensors should have an excellent sensitivity in the range of 0-20 kPa when applied in wearable applications. Traditional pressure sensors cannot achieve both a high sensitivity and a large working range simultaneously, which results in their limited applications in wearable fields. There is an urgent need to develop a pressure sensor to make a breakthrough in both sensitivity and working range. In this paper, a graphene-paper pressure sensor that shows excellent performance in the range of 0-20 kPa is proposed. Compared to most reported graphene pressure sensors, this work realizes the optimization of sensitivity and working range, which is especially suitable for wearable applications. We also demonstrate that the pressure sensor can be applied in pulse detection, respiratory detection, voice recognition, as well as various intense motion detections. This graphene-paper pressure sensor will have great potentials for smart wearable devices to achieve health monitoring and motion detection.
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
A wearable and highly sensitive pressure sensor with ultrathin gold nanowires
Gong, S., Schwalb, W., Wang, Y., Chen, Y., Tang, Y., Si, J., Shirinzadeh, B., & Cheng, W. (2014) · Nature Communications · doi:10.1038/ncomms4132 · Cited by 2,064
Self-citationPan, L., Chortos, A., Yu, G., Wang, Y., Isaacson, S., Allen, R., Shi, Y., Dauskardt, R., & Bao, Z. (2014) · Nature Communications · doi:10.1038/ncomms4002 · Cited by 1,519
Nanowire active-matrix circuitry for low-voltage macroscale artificial skin
Takei, K., Takahashi, T., Ho, J. C., Ko, H., Gillies, A. G., Leu, P. W., Fearing, R. S., & Javey, A. (2010) · Nature Materials · doi:10.1038/nmat2835 · Cited by 1,274
Cited by
Ge, G., Zhang, Y., Shao, J., Wang, W., Si, W., Huang, W., & Dong, X. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201802576 · Cited by 579
Emerging Technologies of Flexible Pressure Sensors: Materials, Modeling, Devices, and Manufacturing
Huang, Y., Fan, X., Chen, S., & Zhao, N. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201808509 · Cited by 543
Graphene-based wearable piezoresistive physical sensors
Zheng, Q., Lee, J.-H., Shen, X., Chen, X., & Kim, J.-K. (2020) · Materials Today · doi:10.1016/j.mattod.2019.12.004 · Cited by 486
All Paper-Based Flexible and Wearable Piezoresistive Pressure Sensor
Gao, L., Zhu, C., Li, L., Zhang, C., Liu, J., Yu, H.-D., & Huang, W. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b07465 · Cited by 365
Wearable and flexible sensors for user-interactive health-monitoring devices
Ha, M., Lim, S., & Ko, H. (2018) · Journal of Materials Chemistry B · doi:10.1039/c8tb01063c · Cited by 352
Metadata
- Authors
- Tao, L.-Q., Zhang, K.-N., Tian, H., Liu, Y., Wang, D.-Y., Chen, Y.-Q., Yang, Y., & Ren, T.-L.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-16
- Volume
- 11
- Issue
- 9
- Pages
- 8790-8795
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2743983659
- Cited by
- 738
- References
- 3
- FWCI
- 26.77
- 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.