Accurate and Robust Static Hydrophobic Contact Angle Measurements Using Machine Learning.

ABSTRACT

We present a machine learning (ML) approach to static contact angle measurement, trained on a large data set (>7.2 million) of half drop contours based on solutions to the Young-Laplace equation where the contact angle is known a priori (removing all sources of error from human input). The data set included the effects of surface roughness, gravity, the size of drop relative to the image, and reflections of the drop on the surface. The presented ML model (valid for contact angles >110°), in combination with a new automated image and contour processing approach, is shown to be more accurate than other methods when benchmarked against an experimental data set, with an estimated error of 1°. The ML model is also 2 orders of magnitude faster at predicting contact angles than Young-Laplace fitting (the current best practice approach). The accuracy and speed of the presented approach provides a viable pathway toward robust and reproducible high-throughput contact angle analysis. This approach, Conan-ML, is open-source and provided for the use and development of new approaches to goniometry.