Fast fabrication of a self-cleaning coating constructed with scallion-like ZnO using a perfect colloidal monolayer enabled by a predictive self-assembly method

Abstract

Dust greatly influences the light transmittance of solar panels and their corresponding photovoltaic (PV) performance. However, this critical issue (dust resistance for PV devices) has not been given much attention. In this work, a new self-cleaning coating is proposed to address this problem. The coating consists of patterned scallion-like zinc oxide (ZnO), which is hydrothermally grown on a colloidal monolayer template. The crystallization and fluorescence properties of the scallion-like ZnO are quite good. This self-cleaning coating can reduce the light reflection of the PV device as well as convert the ultraviolet (UV) photons into visible photons, thus reducing light-induced degradation of amorphous silicon PV devices. Also, the surface of this coating possesses superhydrophobic properties, with a water contact angle (CA) larger than 150° and a sliding angle (SA) less than 10°, after modification with heptadecafluorodecyltrimethoxysilane (HTMS). Most importantly, a predictive self-assembly method enabled by Monte Carlo (MC) simulation is developed to obtain a wafer-scale colloidal monolayer consisting of hexagonal-close-packed polystyrene (PS) spheres. This method combines spin-coating with thermal treatment which plays a key role in forming a high-quality colloidal monolayer. As commonly known, identifying the optimized self-assembly temperature through experiments is a great challenge and no studies are located in the literature investigating predictive self-assembly of the colloidal monolayer. Herein, we develop MC simulation to predict the optimized temperature of the colloidal monolayer self-assembly, which can effectively reduce experimental burden, and then fabricate a wafer-scale colloidal monolayer with high quality for the first time.