The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Wireless Ti3C2Tx MXene Strain Sensor with Ultrahigh Sensitivity and Designated Working Windows for Soft Exoskeletons
Abstract
Emerging soft exoskeletons pose urgent needs for high-performance strain sensors with tunable linear working windows to achieve a high-precision control loop. Still, the state-of-the-art strain sensors require further advances to simultaneously satisfy multiple sensing parameters, including high sensitivity, reliable linearity, and tunable strain ranges. Besides, a wireless sensing system is highly desired to enable facile monitoring of soft exoskeleton in real time, but is rarely investigated. Herein, wireless Ti 3 C 2 T x MXene strain sensing systems were fabricated by developing hierarchical morphologies on piezoresistive layers and incorporating regulatory resistors into circuit designs as well as integrating the sensing circuit with near-field communication (NFC) technology. The wireless MXene sensor system can simultaneously achieve an ultrahigh sensitivity (gauge factor ≥ 14,000) and reliable linearity ( R 2 ≈ 0.99) within multiple user-designated high-strain working windows (130% to ≥900%). Additionally, the wireless sensing system can collectively monitor the multisegment exoskeleton actuations through a single database channel, largely reducing the data processing loading. We finally integrate the wireless, battery-free MXene e-skin with various soft exoskeletons to monitor the complex actuations that assist hand/leg rehabilitation.
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
Stretchable, Skin-Mountable, and Wearable Strain Sensors and Their Potential Applications: A Review
Amjadi, M., Kyung, K., Park, I., & Sitti, M. (2016) · Advanced Functional Materials · doi:10.1002/adfm.201504755 · Cited by 3,202
Highly Stretchable and Sensitive Strain Sensor Based on Silver Nanowire–Elastomer Nanocomposite
Amjadi, M., Pichitpajongkit, A., Lee, S., Ryu, S., & Park, I. (2014) · ACS Nano · doi:10.1021/nn501204t · Cited by 2,350
Ultrasensitive mechanical crack-based sensor inspired by the spider sensory system
Kang, D., Pikhitsa, P. V., Choi, Y. W., Lee, C., Shin, S. S., Piao, L., Park, B., Suh, K.-Y., Kim, T.-I., & Choi, M. (2014) · Nature · doi:10.1038/nature14002 · Cited by 1,626
Soft robotics: Technologies and systems pushing the boundaries of robot abilities
Laschi, C., Mazzolai, B., & Cianchetti, M. (2016) · Science Robotics · doi:10.1126/scirobotics.aah3690 · Cited by 1,570
The grand challenges of Science Robotics
Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., et al. (2018) · Science Robotics · doi:10.1126/scirobotics.aar7650 · Cited by 1,177
Sensitive, High-Strain, High-Rate Bodily Motion Sensors Based on Graphene–Rubber Composites
Boland, C. S., Khan, U., Backes, C., O’Neill, A., McCauley, J., Duane, S., Shanker, R., Liu, Y., Jurewicz, I., Dalton, A. B., et al. (2014) · ACS Nano · doi:10.1021/nn503454h · Cited by 793
A soft robotic exosuit improves walking in patients after stroke
Awad, L. N., Bae, J., O’Donnell, K., De Rossi, S. M. M., Hendron, K., Sloot, L. H., Kudzia, P., Allen, S., Holt, K. G., Ellis, T. D., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aai9084 · Cited by 672
Yu, Y., Nassar, J., Xu, C., Min, J., Yang, Y., Dai, A., Doshi, R., Huang, A., Song, Y., Gehlhar, R., et al. (2020) · Science Robotics · doi:10.1126/scirobotics.aaz7946 · Cited by 655
Significance of Nanomaterials in Wearables: A Review on Wearable Actuators and Sensors
Jayathilaka, W. A. D. M., Qi, K., Qin, Y., Chinnappan, A., Serrano‐García, W., Baskar, C., Wang, H., He, J., Cui, S., Thomas, S. W., et al. (2019) · Advanced Materials · doi:10.1002/adma.201805921 · Cited by 627
Kim, K. K., Hong, S., Cho, H. M., Lee, J., Suh, Y. D., Ham, J., & Ko, S. H. (2015) · Nano Letters · doi:10.1021/acs.nanolett.5b01505 · Cited by 626
Liu, Q., Chen, J., Li, Y., & Shi, G. (2016) · ACS Nano · doi:10.1021/acsnano.6b03813 · Cited by 609
Hwang, B.-U., Lee, J.-H., Trung, T. Q., Roh, E., Kim, D.-I., Kim, S.-W., & Lee, N.-E. (2015) · ACS Nano · doi:10.1021/acsnano.5b01835 · Cited by 502
Cited by
Ti3C2TX MXene for Sensing Applications: Recent Progress, Design Principles, and Future Perspectives
Pei, Y., Zhang, X., Hui, Z., Zhou, J., Huang, X., Sun, G., & Huang, W. (2021) · ACS Nano · doi:10.1021/acsnano.1c00248 · Cited by 799
Flexible MXene‐Based Composites for Wearable Devices
Ma, C., Ma, M., Si, C., Ji, X., & Wan, P. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202009524 · Cited by 544
Chen, W., Liu, L.-X., Zhang, H.-B., & Yu, Z.-Z. (2021) · ACS Nano · doi:10.1021/acsnano.1c01277 · Cited by 293
Metadata
- Authors
- Yang, H., Xiao, X., Li, Z., Li, K., Cheng, N., Li, S., Low, J. H., Jing, L., Fu, X., Achavananthadith, S., Low, F., Wang, Q., Yeh, P.-L., Ren, H., Ho, J. S., Yeow, C.-H., & Chen, P.-Y.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-08-11
- Volume
- 14
- Issue
- 9
- Pages
- 11860-11875
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3048902732
- Cited by
- 158
- References
- 12
- FWCI
- 6.93
- 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.
