Computational Optimized Durable Antireflective Coatings with Liquid-Repellency for Enhanced Photovoltaic Efficiency

Antireflective coatings (ARCs) represent a compelling approach for sustainably improving power conversion efficiency (PCE) in photovoltaic (PV) panels by mitigating optical energy losses arising from surface reflection. Nevertheless, the rational design of ARCs with hierarchical gradient refractive index (GRIN), particularly the further integration of liquid repellency and mechanochemical durability, remains a significant challenge. Here, a computational-experimental synergistic paradigm that concurrently optimizes optical gradients and surface functionalities to address these limitations is demonstrated. Finite-difference time-domain (FDTD) simulations are employed to rationally optimize the size of silica nanoparticles (SNs) for broadband suppression of angular-dependent reflection and scattering. Guided by theoretical modeling, the refractive index profile of SNs is precisely modulated within a tri-layer designed GRIN configuration to facilitate interfacial phase coherence. A fluoride-free omniphobic modification is introduced via sequential silane vapor deposition and subsequent polysiloxane grafting. This functionalization endows the liquid-repellent GRIN antireflective coating (LGAC) coated glass with a peak transmittance of ≈99.5% within the visible spectrum, while simultaneously imparting self-cleaning properties. Furthermore, LGAC contributes to a relative PCE enhancement exceeding 7.1% in PV cells. Its exceptional mechanochemical durability positions it as a promising candidate for enduring performance enhancement and protection of PV panels under variable environmental conditions.