Journal article
Accurate and Robust Static Hydrophobic Contact Angle Measurements Using Machine Learning
DG Shaw, R Liang, T Zheng, J Qi, JD Berry
Langmuir | Published : 2024
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°. Th..
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Awarded by Australian Government
Funding Acknowledgements
This work was performed in part at the Materials Characterisation and Fabrication Platform (MCFP) at the University of Melbourne. This research was supported by Research Computing Services, the NCI Access scheme, and the Petascale Campus Initiative at the University of Melbourne. Experimental data was provided by Prof. Chiara Neto, Dr Isaac Gresham, and Mr Anthony Katselas from the School of Chemistry at the University of Sydney; Dr Binyu Zhao from the Leibniz Institute of Polymer Research; Dr Avinash Ashok and Prof. Raymond Dagastine from the Department of Chemical Engineering at the University of Melbourne; Ms Kristina Mielke and Prof. Andreas Jupke from Rheinisch-Westfaelische Technische Hochschule Aachen University; Dr Samuel Cheeseman and Prof. David Nisbet from the Department of Biomedical Engineering at the University of Melbourne. JDB is the recipient of an Australian Research Council Future Fellowship (FT220100319) funded by the Australian Government. Daniel G. Shaw is the recipient of a University of Melbourne Research Training Program scholarship.