Ab-initio-based interface modeling and statistical analysis for estimate of the water contact angle on a metallic Cu(111) surface
Ab-initio-based interface modeling and statistical analysis for estimate of the water contact angle on a metallic Cu(111) surface
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DOI:
10.1016/j.surfin.2022.102342
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发表时间:
2020-09
影响因子:
6.2
通讯作者:
T. Murono;K. Hongo;K. Nakano;R. Maezono
中科院分区:
文献类型:
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作者:
T. Murono;K. Hongo;K. Nakano;R. Maezono
Controlling the water contact angle on a surface is important for regulating its wettability in industrial applications, which involves developingab initioprediction 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. Previousab initiostudies 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, focuses on the water contact angle on a metallic surface, and proposes a simpleab initiobased estimation scheme. We not only adopt the previously proposed structural modeling based on the periodic-honeycomb array, but also consider an ensemble of isolated water oligomers that have different molecular coverage (ML) values. We established a statistic model to predict a contact angle of the water wetting on a Cu(111) surface: The coverage-dependent contact angles obtained from each of the isolated clusters were fit to a quadratic regression, and the contact angle was interpolated by referring to a ML value of water layer in literature. This interpolated value lies within the deviation of experimental angles. In addition, the Boltzmann-average over the isolated clusters was found to agree well with the interpolated one. This indicates that the Boltzmann-average is useful for estimating the contact angle of other metallic surfaces without knowing a ML valuea priori.