Machine learning model for understanding laser superhydrophobic surface functionalization

A general machine learning (ML) framework of surface wetting is proposed by considering a broad range of factors, including solid surface topography, solid surface chemistry, liquid properties, and environmental conditions. In particular, an XGBoost-based ML model is demonstrated for learning the surface wetting behaviors processed by a laser-based surface functionalization process, namely nanosecond laser-based high-throughput surface nanostructuring (nHSN). This is the first known attempt to apply machine learning to surface wetting by considering both surface topography and surface chemistry properties. Novel microscale and nanoscale topography parameters viz., roughness, fractal, entropy, feature periodicity are defined with suitable computer algorithms to comprehensively describe the surface topography. A novel set of surface chemistry parameters such as polarity, volume, and amount of functional groups are also used as the machine learning model input. Upon analyzing the importance of each parameter for the nHSN process, surface chemistry shows the greatest importance in determination of surface wettability, while surface morphology also plays a part in influencing the wettability.

Automated summary

A general machine learning framework for surface wetting, considering factors such as solid surface topography, chemistry, liquid properties, and environmental conditions, is proposed. An XGBoost-based model is demonstrated for studying surface wetting behaviors processed by laser-based surface functionalization. The importance of surface chemistry in determining wettability is highlighted, while surface morphology also plays a role in influencing wetting behavior.