The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity
Open accessAbstract
Sensitivity is a crucial parameter for flexible pressure sensors and electronic skins. While introducing microstructures (e.g., micro-pyramids) can effectively improve the sensitivity, it in turn leads to a limited pressure-response range due to the poor structural compressibility. Here, we report a strategy of engineering intrafillable microstructures that can significantly boost the sensitivity while simultaneously broadening the pressure responding range. Such intrafillable microstructures feature undercuts and grooves that accommodate deformed surface microstructures, effectively enhancing the structural compressibility and the pressure-response range. The intrafillable iontronic sensor exhibits an unprecedentedly high sensitivity (Smin > 220 kPa−1) over a broad pressure regime (0.08 Pa-360 kPa), and an ultrahigh pressure resolution (18 Pa or 0.0056%) over the full pressure range, together with remarkable mechanical stability. The intrafillable structure is a general design expected to be applied to other types of sensors to achieve a broader pressure-response range and a higher sensitivity.
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
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
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
Hammock, M. L., Chortos, A., Tee, B. C. ‐., Tok, J. B. ‐., & Bao, Z. (2013) · Advanced Materials · doi:10.1002/adma.201302240 · Cited by 2,396
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
Learning the signatures of the human grasp using a scalable tactile glove
Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162
Cited by
Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics
Heng, W., Solomon, S., & Gao, W. (2022) · Advanced Materials · doi:10.1002/adma.202107902 · Cited by 609
CRISPR-Cas12a-driven MXene-PEDOT:PSS piezoresistive wireless biosensor
Zeng, R., Wang, W., Chen, M., Wan, Q., Wang, C., Knopp, D., & Tang, D. (2021) · Nano Energy · doi:10.1016/j.nanoen.2020.105711 · Cited by 394
Cutaneous Ionogel Mechanoreceptors for Soft Machines, Physiological Sensing, and Amputee Prostheses
Shen, Z., Zhu, X., Majidi, C., & Gu, G. (2021) · Advanced Materials · doi:10.1002/adma.202102069 · Cited by 291
Progress of flexible strain sensors for physiological signal monitoring
Shen, Z., Liu, F., Huang, S., Wang, H., Yang, C., Hang, T., Tao, J., Xia, W., & Xie, X. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114298 · Cited by 250
Yuan, H., Zhang, Q., Zhou, T., Wu, W., Li, H., Yin, Z., Ma, J., & Jiao, T. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.149926 · Cited by 180
Metadata
- Authors
- Bai, N., Wang, L., Wang, Q., Deng, J., Wang, Y., Lu, P., Huang, J., Li, G., Zhang, Y., Yang, J., Xie, K., Zhao, X., & Guo, C. F.
- Venue
- Nature Communications
- Published
- 2020-01-10
- Volume
- 11
- Issue
- 1
- Pages
- 209-209
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2999408882
- Cited by
- 810
- References
- 6
- FWCI
- 29.27
- 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: Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. https://app.cochat.ai/papers/10.1038/s41467-019-14054-9
- The paper
- Bai, N., Wang, L., Wang, Q., Deng, J., Wang, Y., Lu, P., Huang, J., Li, G., Zhang, Y., Yang, J., Xie, K., Zhao, X., & Guo, C. F. (2020). Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. Nature Communications. https://doi.org/10.1038/s41467-019-14054-9
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.