Optimization of Plasmonic-Assisted Osmotic Energy Conversion using Physics-Informed Genetic Algorithm Toward Real-World Applications

초록

This study presents a high-efficiency hybrid energyharvesting platform that synergistically couples solar and water resources through a plasmonic-assisted osmotic system built on a 3D nanochannel-network membrane (NCNM). A physics-informed genetic algorithm (GA), coupled with finite-element modeling (FEM), was used to systematically optimize channel geometry and surface charge distribution. The optimized NCNM exhibited pronounced ion current rectification and significantly enhanced power density, with experimental measurements in close agreement with FEM predictions, validating the robustness of the design framework. Plasmonic excitation further amplified ionic transport, while broadband solar illumination outperformed monochromatic excitation by simultaneously activating multiple resonance modes. Overall, this work establishes a scalable and versatile strategy for next-generation hybrid energy harvesting, offering strong potential for deployment in self-powered environmental and sensing systems.