Data-driven learning of process−property−performance relation in laser-induced aqueous manufacturing and integration of ZnO piezoelectric nanogenerator for self-powered nanosensors

Piezoelectric nanogenerators have attracted intensive interest in harvesting the stray mechanical energy environment to power miniaturized electronics and sensors. However, their efficient integration into systems compatibility with existing technologies for practical applications remains challenging. Here, we report first time systematic, data-driven learning of process-property-performance relation ZnO nanowires piezoelectric that are synthesized integrated through a laser-induced chemical process. An experiment-derived behavioral model was established reveal apparent connections between production parameters output performance nanogenerator. We further demonstrated application such knowledge integrating optimized nanogenerator photosensor self-powered sensor system, exhibiting potential future system-level improvements.