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

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