A non-printed integrated-circuit textile for wireless theranostics

Yuxin Yang, Xiaofei Wei, Nannan Zhang (), Juanjuan Zheng, Xing Chen, Qian Wen, Xinxin Luo, Chong-Yew Lee, Xiaohong Liu, Xingcai Zhang (), Jun Chen, Changyuan Tao, Wei Zhang () and Xing Fan ()
Additional contact information
Yuxin Yang: Chongqing University
Xiaofei Wei: Chongqing University
Nannan Zhang: Chongqing University
Juanjuan Zheng: Harvard University
Xing Chen: Harvard University
Qian Wen: Chongqing University
Xinxin Luo: Industrial Technology Research Institute of Chongqing University
Chong-Yew Lee: University Sains Malaysia
Xiaohong Liu: Chinese Academy of Sciences
Xingcai Zhang: Harvard University
Jun Chen: University of California, Los Angeles
Changyuan Tao: Chongqing University
Wei Zhang: Chinese Academy of Sciences
Xing Fan: Chongqing University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI “nurse” for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.

Date: 2021
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DOI: 10.1038/s41467-021-25075-8

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