D3SAI: a data-driven platform for measuring interfacial tension using machine learning and drop shape analysis

Abstract Interfacial tension in biphasic systems plays a key role across many industrial processes. We present a Data-Driven Drop Shape Analysis method (D3SAI) that uses XGBoost to accurately estimate interfacial tension. D3SAI uses a pendant drop image of a biphasic liquid as an input and determines the interfacial tension through image processing, feature extraction, and machine learning. The accuracy of each step has been evaluated using both synthetic and experimental pendant drops. D3SAI implements a traditional drop shape analysis approach to generate a large library of synthetic pendant drops. This step is necessary to support reliable model training. Then, certain physical properties of the drop profile are extracted to represent the shape characteristics of the pendant drops. The extracted features are used to train the XGBoost model to predict interfacial tension. Using drop features rather than coordinates significantly reduces the input size and as a result the cost of computation in the training process. This makes D3SAI easier to retrain on large datasets for a variety of drop shapes and applications. D3SAI estimates the interfacial tension of well-deformed drops with less than 1.2% inaccuracy. Tests on experimental images confirm that D3SAI provides consistent and accurate results, making it suitable for large-scale measurements. Moreover, D3SAI predicts the surface tension of less-deformed (circular) drops with less than 8% inaccuracy. Although less-deformed drops are not ideal for surface tension measurements, they are sometimes necessary, for example, when working with ultra-low tension systems.