Design of low-thickness random nanoparticle coatings for radiative cooling using particle swarm optimization

Radiative cooling technology has garnered significant attention from both researchers and industries due to its promising potential to mitigate global warming. While most previous studies focused on the spectral optical performance, industrial application needs to balance the radiative cooling performance and coating thickness. However, there still lacks fundamental understanding on the optimum design of random nanoparticle coatings, including solar reflectivity upper limit and optimum design under different thicknesses. Herein, we combine theoretical analysis, experiment and optimizing algorithm to investigate the radiative cooling performance of low-thickness nanoparticle-based coatings. We first derive an analytical expression based on radiative transfer equations to explore the upper limit of reflection for random nanoparticle coatings and identify the critical parameters governing the thin coating design. Then we validate the Monte Carlo simulation against experiment. Combining the particle swarm optimization and Monte Carlo simulation, we further determine the optimal design parameters—material selection, particle radius, and volume fraction—and the corresponding maximum solar reflectivity. Our results demonstrate that at a thickness of 10 μm, high-refractive-index TiO 2 achieves a maximum solar reflectivity of 76.0 %. For greater thicknesses, non-absorbing h -BN proves to be more effective, achieving reflectivity ranging from 84.3 % at 20 μm to 96.1 % at 200 μm. These findings provide valuable guidelines for designing thin radiative cooling coatings to mitigate global warming.