Salinity gradient energy harvesting via tuned ionic nanotransistor geometries

Osmotic energy harvesting using nanofluidic reverse electrodialysis has emerged as a transformative strategy for sustainable power generation. However, enhancing energy conversion efficiency remains a critical challenge due to competing tradeoffs between ionic selectivity and conductance. In this work, we present a comprehensive numerical investigation of ionic nanotransistors featuring negative–positive–negative-type channel architectures coated with polyelectrolyte soft layers. A fully coupled Poisson–Nernst–Planck and Navier–Stokes multiphysics framework was developed to unravel the intricate interplay between nanochannel geometry, soft layer charge density NPEL, and imposed salinity gradients CH/CL. Six distinct geometries, such as bullet, conical, cylindrical, dumbbell, funnel, and trumpet-shaped, were systematically explored in terms of their performance under steady-state conditions. Key electrokinetic metrics, including diffusion potential Ediff, osmotic current IOS, cation transference number t+, maximum power output Pmax, and energy conversion efficiency (ηmax), were quantified and analyzed. Results show that trumpet-shaped channels exhibit superior electrostatic gating and ion selectivity, achieving Ediff values up to 145 mV, t+ exceeding 0.97, and ηmax surpassing 45% under moderate salinity gradients CH/CL≈100. In contrast, bullet-shaped geometries maximize conductance and attain Pmax > 6.5 pW, albeit at lower efficiency. Increasing NPEL intensifies electrostatic exclusion and cationic enrichment, particularly in funnel and trumpet morphologies, thereby boosting both Ediff and ηmax. Conversely, geometries with constrictions, such as dumbbell-shaped channels, exhibit performance degradation at high NPEL due to ion depletion and over-screening effects. This study reveals the critical role of geometry, soft layer synergy in regulating nanoscale ion transport and optimizing osmotic-to-electric energy transduction. By directly addressing the needs of advanced desalination systems, this modeling platform facilitates the optimized design of ionic nanotransistors for enhanced nanofluidic energy conversion.