TY - JOUR
T1 - Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition
AU - Zhao, Pengfei
AU - Song, Yilin
AU - Hu, Zhipeng
AU - Zhong, Zihan
AU - Li, Yi
AU - Zhou, Kui
AU - Qin, Tingting
AU - Yan, Yan
AU - Hsu, Hsiao Hsuan
AU - Han, Su Ting
AU - Roy, Vellaisamy A.L.
AU - Kuo, Chi Ching
AU - Zhou, Ye
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/11
Y1 - 2024/11
N2 - Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.
AB - Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.
KW - Artificial Intelligence
KW - Biodegradable electronics
KW - Deep learning
KW - Green textiles
KW - Pressure sensor
UR - http://www.scopus.com/inward/record.url?scp=85201747316&partnerID=8YFLogxK
U2 - 10.1016/j.nanoen.2024.110118
DO - 10.1016/j.nanoen.2024.110118
M3 - Article
AN - SCOPUS:85201747316
SN - 2211-2855
VL - 130
JO - Nano Energy
JF - Nano Energy
M1 - 110118
ER -