Smart Nanofluidic Transistors for High-Efficiency Blue Energy Harvesting: The Hidden Role of Ion Hydration

Osmotic energy harvesting from salinity gradients has emerged as a promising route for sustainable power generation; however, achieving high power output and efficiency remains limited by ionic selectivity and internal resistance in conventional membranes. In this work, we investigate osmotic energy conversion in polymer-coated ionic nanotransistors under high salinity gradients using a coupled Poisson-Nernst-Planck and Navier-Stokes numerical framework. The influence of cation type in three monovalent electrolytes (NaCl, KCl, and LiCl) is systematically examined for two soft-gated nanotransistor configurations (NPN and PNP). A key innovation of this study is the identification of a configuration-dependent inversion in electrolyte performance, arising from the interplay between ion hydration, diffusivity, and electrostatic partitioning within the soft polyelectrolyte layer. Under a concentration ratio of 1000 and a polyelectrolyte charge density of 100 mol m^-3, the NPN configuration exhibits the highest power output with KCl (5.27 pW; ∼186 W/m²), which is approximately three times higher than NaCl (1.75 pW; ∼61.7 W/m²) and over ten times higher than LiCl (0.51 pW; ∼18 W/m²). In contrast, the PNP configuration demonstrates a markedly enhanced maximum power output with LiCl, reaching 9.78 pW (∼345 Wm²), which is ∼36% higher than NaCl (7.19 pW; ∼254 W/m²) and nearly 2.6 times higher than KCl (3.81 pW; ∼134 W/m²). This performance inversion originates from stronger electrostatic coupling and larger hydration radius of Li⁺ in the PNP configuration, which enhance ionic selectivity and suppress co-ion leakage, whereas the higher mobility of K⁺ benefits the conductance-dominated NPN system. Furthermore, the peak energy conversion efficiency approaches ∼50% in the PNP-LiCl system, highlighting the high thermodynamic favorability of salinity-gradient-driven nanofluidic power generation. These findings provide mechanistic insight and quantitative design guidance for optimizing electrolyte selection and surface-charge architectures in next-generation nanofluidic osmotic energy-harvesting devices.