Ultra-High Conversion Efficiencies of Electrical Energy for Water Nanodroplets Impinging on Graphene Surfaces

Abstract

Achieving a high electricity conversion efficiency is of paramount importance in the development of droplet-based generators. By combining first-principles calculations, a density-functional-theory based machine learning technique, and large-scale molecular dynamics simulations, we have designed an ideal nanodroplet-based generator composed of graphene, h-BN and Cu electrodes, in which electricity is generated through the impingement of water nanodroplets on the graphene surface. The energy conversion efficiencies for converting kinetic energy into electrical energy exceed 46% for nanodroplets with diameters ranging from 3 to 30 nm. Notably, a peak efficiency of 91% was achieved for a 6 nm nanodroplet. These ultra-high conversion efficiencies can be primarily attributed to the strong charge exchange and transfer occurring at the water/graphene interfaces, as well as the remarkably high charge densities induced in the graphene layers. Our results highlight a highly promising way to improve and enhance the electrical energy conversion efficiency by the utilization of water nanodroplets.

Keywords: Ultra-high conversion efficiency, nanodroplet-based generator, graphene, density functional theory, Machine learning