DOI: 10.1021/acsami.6c07918 ISSN: 1944-8244

Plasmon-Enhanced Osmotic Energy Conversion via Physics-Informed Evolution of Nanochannel Networks

Gyubin Park, Syed Muhammad Anas Ibrahim, Jeewon Shin, Junghun Bae, Heonjae Jeong, Jungyul Park

Abstract

The global pursuit of carbon neutrality has accelerated the development of reusable, environmentally sustainable energy technologies capable of delivering stable power generation. In this context, nano-/microfluidic energy-conversion platforms have attracted increasing attention owing to their high energy-harvesting efficiency arising from enhanced ion transport and interfacial phenomena in nanoconfined environments. Despite these advantages, their effective integration with complementary energy resources remains insufficiently explored. Here, we present a plasmon-enhanced osmotic energy-conversion platform that synergistically couples plasmon-induced surface charge modulation with salinity-driven ion transport to maximize power generation. Plasmon-induced surface charge amplification strengthens ion transport, selectivity, and diffusivity, thereby improving energy-conversion performance. To further boost efficiency, a physics-informed genetic algorithm integrated with multiphysics simulations was employed to design a bipolar three-dimensional nanochannel network membrane (3D NCNM), enabling systematic optimization of ionic rectification and transport asymmetry. The optimized geometry promotes asymmetric ion transport and effective surface charge regulation, thereby enhancing osmotic energy-conversion efficiency. The synergistic effects of plasmonic enhancement and structural optimization are experimentally validated under 532 nm laser irradiation, resulting in substantial increases in open-circuit voltage, current density, and power density. Furthermore, broadband solar illumination provides additional performance gains, indicating that spectrally distributed plasmonic excitation activates multiple resonance modes to collectively enhance ionic transport. Finally, the optimized NCNM is implemented in a scalable 5 × 5 series–parallel array, demonstrating stable power output in an integrated device configuration.

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