功率密度
杰纳斯
材料科学
离子
渗透力
膜
纳米技术
能量收集
极化(电化学)
离子运输机
工作(物理)
密度泛函理论
光电子学
电压
化学物理
能量转换
电流密度
扩散
分子动力学
浓差极化
相对湿度
化学工程
电势能
碳纳米管
电场
湿度
化学
纳米流体学
能量转换效率
流动电流
传质
静电纺丝
作者
Zhiwei Zhao,Jin Fang,Jinjun Shao,Yunhong Chen,Aixiu Yu,Qingqing Ni,Zhenzhen Xu
标识
DOI:10.1021/acssuschemeng.6c04522
摘要
Moist-electric generators (MEGs) have emerged as a pivotal technology for sustainable energy harvesting and self-powered sensing, offering a carbon-neutral solution to the global energy crisis. However, conventional MEGs predicated on ion diffusion are often constrained by suboptimal power densities and a lack of inherent biodegradability, hindering their practical integration. Herein, we report a high-performance Janus-structured MEG fabricated via electrospinning, comprising a poly(vinyl alcohol)/phytic acid (PVA/PA) top layer and a LiCl-doped (PVA/PA-LiCl) bottom layer. The intrinsic hygroscopicity gradient within the Janus membrane facilitates directional ion migration, thereby significantly augmenting the electrical output. Density functional theory (DFT) calculations and molecular dynamics (MD) simulations reveal that the incorporation of LiCl intensifies the polarization effect and moisture affinity while narrowing the electronic bandgap, providing a fundamental driving force for the enhanced performance. At 97% relative humidity (RH), the device achieves an open-circuit voltage ( V OC ) of 0.96 V, a short-circuit current density ( I SC ) of 11.1 μA, and a maximum power density ( P max ) of 2.56 μW/cm 2 . Beyond energy generation, the device enables noncontact humidity sensing, information encoding, and monitoring of human motion and respiration, with a machine learning-assisted humidity recognition accuracy of 97.4%. Moreover, the membrane exhibits rapid degradation in soil within 120 h, indicating its potential environmental compatibility. This work provides a Janus membrane design strategy for enhancing ion transport in moisture-electric generators and broadens the design space for nanofiber-based MEGs.
科研通智能强力驱动
Strongly Powered by AbleSci AI