ACS applied materials & interfaces · 2025
High-Performance Mechano-Sensitive Piezoelectric Nanogenerator from Post-Treated Nylon-11,11 Textiles for Energy Harvesting and Human Motion Monitoring.
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Wang Zhixiao, Duan Yubo, Liu Chongyang, Wang Lihua, Zhang Zhaoyang, Zhao Wei, Zhang Xiaomeng, Zhang Yuancheng, Fu Peng, Cai Hongling, Cui Zhe, Pang Xinchang, Dong Zhi Li, Liu Minying
Аннотация
Piezoelectric polymer textiles offer distinct advantages in the fabrication of wearable nanogenerators (NGs). One effective strategy to enhance the output capacity of NGs is to modulate the piezoelectric performance of the textiles. This paper focuses on further improving the piezoelectric properties of nylon-11,11 textiles through post-drawing and annealing treatments. We elucidate the evolution of morphology and the ferroelectric phase in the submicron/nanoscale fibers during post processing as well as the corresponding changes in performance. The drawing process primarily enhances the orientation of the crystalline phase and reduces the fiber diameter, while the annealing process more effectively promotes the crystal size and crystallinity. Afterward, we propose an optimal postdrawing and annealing assisted-electrostatic spinning process. Under the synergistic effects of these post-treatments, the remanent polarization (Pr) of nylon-11,11 textile increased to 4.7 times that of the untreated textile, resulting in amplified piezoelectric outputs. The output voltage, current, and power density of the prepared PENG reached 21.5 V, 800 nA, and 1.88 mW·m-2 (80 MΩ), respectively. Notably, at pressures exceeding 8 kPa, the mechano-voltage and current sensitivity reached as high as 266 mV/kPa and 13.99 nA/kPa, respectively, which is extraordinary compared to other piezoelectric NGs and comparable to the performance of nylon-based triboelectric NGs. Furthermore, we investigated the potential application of the prepared PENG in biomechanical energy harvesting and human movement monitoring. Experiments demonstrated its effectiveness in powering light bulbs, tracking walking status, and monitoring finger/hand/wrist gestures.