Ab-initio-based Interface Modeling and Statistical Analysis for Estimate of the Water Contact Angle on a Metallic Cu(111) Surface
Takahiro Murono, Kenta Hongo, Kousuke Nakano, Ryo Maezono

TL;DR
This paper develops an ab initio-based statistical model to predict water contact angles on metallic Cu(111) surfaces, incorporating structural models and ensemble averaging to improve accuracy and applicability.
Contribution
It introduces a novel estimation scheme for water contact angles on metals using ab initio modeling and statistical averaging over water oligomers, extending previous insulator-focused methods.
Findings
The quadratic regression model accurately predicts contact angles within experimental deviations.
Boltzmann-averaged contact angles align well with interpolated values, validating the approach.
The method can estimate contact angles on other metallic surfaces without prior coverage data.
Abstract
Controlling the water contact angle on a surface is important for regulating its wettability in industrial applications, which involves developing ab initio prediction scheme of accurately predicting the angle. The scheme requires structural models for the adsorption of liquid molecules on a surface, but their reliability depend on whether the surfaces comprise insulating or metallic materials. Previous ab initio studies have focused on the estimation of the water contact angle on insulators, where the periodic-honeycomb array of water molecules was adopted as the adsorption model for the water on the insulating surface and succeeded in the insulating cases. This study, however, focus on the water contact angle on a metallic surface, and propose a simple ab initio based estimation scheme. We not only adopt the previously proposed structural modeling based on the periodic-honyecomb…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
