The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Capacitive Pressure Sensor with High Sensitivity and Fast Response to Dynamic Interaction Based on Graphene and Porous Nylon Networks
Abstract
Flexible pressure sensors are of great importance to be applied in artificial intelligence and wearable electronics. However, assembling a simple structure, high-performance capacitive pressure sensor, especially for monitoring the flow of liquids, is still a big challenge. Here, on the basis of a sandwich-like structure, we propose a facile capacitive pressure sensor optimized by a flexible, low-cost nylon netting, showing many merits including a high response sensitivity (0.33 kPa –1 ) in a low-pressure regime (<1 kPa), an ultralow detection limit as 3.3 Pa, excellent working stability after more than 1000 cycles, and synchronous monitoring for human pulses and clicks. More important, this sensor exhibits an ultrafast response speed (<20 ms), which enables its detection for the fast variations of a small applied pressure from the morphological changing processes of a droplet falling onto the sensor. Furthermore, a capacitive pressure sensor array is fabricated for demonstrating the ability to spatial pressure distribution. Our developed pressure sensors show great prospects in practical applications such as health monitoring, flexible tactile devices, 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
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
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
A flexible and highly sensitive strain-gauge sensor using reversible interlocking of nanofibres
Pang, C., Lee, G.-Y., Kim, T.-I., Kim, S. M., Kim, H. N., Ahn, S.-H., & Suh, K.-Y. (2012) · Nature Materials · doi:10.1038/nmat3380 · Cited by 1,695
Pan, 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
Monitoring of Vital Signs with Flexible and Wearable Medical Devices
Khan, Y., Ostfeld, A. E., Lochner, C. M., Pierre, A., & Arias, A. C. (2016) · Advanced Materials · doi:10.1002/adma.201504366 · Cited by 1,408
Recent Advances in Flexible and Stretchable Bio‐Electronic Devices Integrated with Nanomaterials
Choi, S., Lee, H., Ghaffari, R., Hyeon, T., & Kim, D. (2016) · Advanced Materials · doi:10.1002/adma.201504150 · Cited by 1,104
Liu, Q., Chen, J., Li, Y., & Shi, G. (2016) · ACS Nano · doi:10.1021/acsnano.6b03813 · Cited by 609
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
Joo, Y., Byun, J., Seong, N., Ha, J., Kim, H., Kim, S., Kim, T., Im, H., Kim, D., & Hong, Y. (2015) · Nanoscale · doi:10.1039/c5nr00313j · Cited by 378
Cited by
Flexible all-textile dual tactile-tension sensors for monitoring athletic motion during taekwondo
Ma, Y., Ouyang, J., Raza, T., Li, P., Jian, A., Li, Z., Liu, H., Chen, M., Zhang, X., Qu, L., et al. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.105941 · Cited by 145
Vu, C. C., & Kim, J. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.112029 · Cited by 109
Li, X., Liu, Y., Ding, Y., Zhang, M., Lin, Z., Hao, Y., Li, Y., & Chang, J. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c01042 · Cited by 79
Metadata
- Authors
- He, Z., Chen, W., Liang, B., Liu, C., Yang, L., Lu, D., Mo, Z., Zhu, H., Tang, Z., & Gui, X.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-03-27
- Volume
- 10
- Issue
- 15
- Pages
- 12816-12823
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2794972024
- Cited by
- 289
- References
- 9
- FWCI
- 12.93
- 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.
