The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Knitted Strain Sensor Textiles of Highly Conductive All-Polymeric Fibers
Open accessAbstract
A scaled-up fiber wet-spinning production of electrically conductive and highly stretchable PU/PEDOT:PSS fibers is demonstrated for the first time. The PU/PEDOT:PSS fibers possess the mechanical properties appropriate for knitting various textile structures. The knitted textiles exhibit strain sensing properties that were dependent upon the number of PU/PEDOT:PSS fibers used in knitting. The knitted textiles show sensitivity (as measured by the gauge factor) that increases with the number of PU/PEDOT:PSS fibers deployed. A highly stable sensor response was observed when four PU/PEDOT:PSS fibers were co-knitted with a commercial Spandex yarn. The knitted textile sensor can distinguish different magnitudes of applied strain with cyclically repeatable sensor responses at applied strains of up to 160%. When used in conjunction with a commercial wireless transmitter, the knitted textile responded well to the magnitude of bending deformations, demonstrating potential for remote strain sensing applications. The feasibility of an all-polymeric knitted textile wearable strain sensor was demonstrated in a knee sleeve prototype with application in personal training and rehabilitation following injury.
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Citations
Cites
Wearable Electronics and Smart Textiles: A Critical Review
Stoppa, M., & Chiolerio, A. (2014) · Sensors · doi:10.3390/s140711957 · Cited by 2,107
Highly Stretchable Piezoresistive Graphene–Nanocellulose Nanopaper for Strain Sensors
Yan, C., Wang, J., Kang, W., Cui, M., Wang, X., Foo, C. Y., Chee, K. J., & Lee, P. S. (2014) · Advanced Materials · doi:10.1002/adma.201304742 · Cited by 1,179
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
Cited by
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375
Electrically conductive polymer composites for smart flexible strain sensors: a critical review
Liu, H., Li, Q., Zhang, S., Yin, R., Liu, X., He, Y., Dai, K., Shan, C., Guo, J., Liu, C., et al. (2018) · Journal of Materials Chemistry C · doi:10.1039/c8tc04079f · Cited by 709
Application Challenges in Fiber and Textile Electronics
Wang, L., Fu, X., He, J., Shi, X., Chen, T., Chen, P., Wang, B., & Peng, H. (2020) · Advanced Materials · doi:10.1002/adma.201901971 · Cited by 477
Ren, J., Wang, C., Zhang, X., Carey, T., Chen, K., Yin, Y., & Torrisi, F. (2017) · Carbon · doi:10.1016/j.carbon.2016.10.045 · Cited by 365
Functional Fiber Materials to Smart Fiber Devices
Chen, C., Feng, J., Li, J., Guo, Y., Shi, X., & Peng, H. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00192 · Cited by 308
Textile‐Based Strain Sensor for Human Motion Detection
Wang, J., Lu, C., & Zhang, K. (2020) · ENERGY & ENVIRONMENTAL MATERIALS · doi:10.1002/eem2.12041 · Cited by 294
Textile Technology for Soft Robotic and Autonomous Garments
Sanchez, V., Walsh, C. J., & Wood, R. J. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202008278 · Cited by 292
Porous Conductive Textiles for Wearable Electronics
Ding, Y., Jiang, J., Wu, Y., Zhang, Y., Zhou, J., Zhang, Y., Huang, Q., & Zheng, Z. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00507 · Cited by 269
Trung, T. Q., Le, H. S., Dang, T. M. L., Ju, S., Park, S. Y., & Lee, N. (2018) · Advanced Healthcare Materials · doi:10.1002/adhm.201800074 · Cited by 232
Zhou, Y., Sun, Y., Li, Y., Shen, C., Lou, Z., Min, X., & Stewart, R. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202400124 · Cited by 17
Metadata
- Authors
- Seyedin, S., Razal, J. M., Innis, P. C., Jeiranikhameneh, A., Beirne, S., & Wallace, G. G.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-09-15
- Volume
- 7
- Issue
- 38
- Pages
- 21150-21158
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W1244932852
- Cited by
- 331
- References
- 3
- FWCI
- 10.22
- Type
- journal article
- Language
- en
- Open access
- green
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