Sprecher
Beschreibung
Wetting is generally regarded as an inherent property of material surfaces. Wetting imperfections contribute to contact angle hysteresis, influencing the overall wetting dynamics. Existing techniques like contact angle goniometry are not sufficient for accurately quantifying variations in wetting behavior due to temporal and local resolution limitations. A direct quantitative measurement of frictional forces during drop sliding on surfaces was recently proposed by Hinduja et al. [1]. However a direct comparison of measured forces with calculated forces by the Furmidge equation is hampered as the drop width is unknown. According to the Furmidge equation, drop width is directly proportional to the frictional force, making it a pivotal factor in generating surface-wetting maps. An additional camera for drop width imaging can be installed. The latter adds significant optical and data recording complexity to the setup.
Here we report on applying machine learning models to estimate the drop width on solid surfaces using only side-view videos obtained from scanning drop friction force microscopy (sDoFFI). The primary objective is to predict the drop width with high precision, this will enable a deeper understanding of the forces involved in drop dynamics and drop electrification at defects and wetting imperfections. We are determining the contact angles (CA) by analyzing the video data obtained by raster scanning of drop on Kruss DSA100 goniometer using a Python script. CAs are calculated by marking a line at the solid-liquid interface called baseline within the code and applying the tangent fit. The friction force acting on the moving contact line is plotted against the position of the drop by quantifying the deflection of the capillary from its resting position using developed Python code. In addition, the need for an additional high speed camera for the DoFFI setup is eliminated which, significantly reduces the systems complexity and costs while maintaining functionality to enhance the industrial applicability of the DoFFI setup.
References:
[1] Hinduja, Chirag, Alexandre Laroche, Sajjad Shumaly, Yujiao Wang, Doris Vollmer, Hans-Jürgen Butt, and Rudiger Berger. "Scanning drop friction force microscopy." Langmuir 38, no. 48 (2022): 14635-14643.