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Contact Angle

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Machine learning enabled orthogonal camera goniometry for accurate and robust contact angle measurements.

Hossein Kabir¹, Nishant Garg²

¹Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Scientific Reports

|January 27, 2023

Summary

This study introduces a novel setup using Convolutional Neural Networks (CNNs) to accurately measure surface wettability by estimating Contact Angle (CA). The method overcomes limitations of traditional techniques, offering improved stability and precision for diverse surfaces and drop types.

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Calibration Procedures for Orthogonal Superposition Rheology

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Area of Science:

Materials Science
Surface Chemistry
Computational Methods

Background:

Surface wettability is crucial for various scientific processes.
Conventional methods struggle with accurate Contact Angle (CA) estimation, especially on hydrophilic surfaces, due to optical distortions.
Existing goniometers exhibit limitations in handling image blurring and diverse drop characteristics.

Purpose of the Study:

To develop an advanced setup utilizing Convolutional Neural Networks (CNNs) for precise Contact Angle (CA) estimation.
To enhance the accuracy and stability of wettability characterization, particularly for challenging surfaces and conditions.
To provide a more robust and versatile alternative to conventional goniometry.

Main Methods:

An original orthogonal camera goniometer setup was developed.
Convolutional Neural Networks (CNNs) were employed for Contact Angle (CA) estimation.
The algorithm was trained on 3375 images and tested for stability against synthetic blurring (Gaussian Blurring up to 22).

Main Results:

The developed CNN-based algorithm demonstrated superior stability against image blurring compared to existing goniometers.
The technique accurately analyzes drops of various colors and chemistries on diverse surfaces.
The automated goniometer showed significantly lower average standard deviation (6.7° vs. 14.6°) and coefficient of variation (14.9% vs. 29.2%).

Conclusions:

The proposed CNN-coupled goniometer offers a more accurate and stable method for surface wettability assessment.
This technique effectively analyzes non-spherical drops on heterogeneous surfaces, overcoming limitations of previous methods.
The system provides a significant advancement in contact angle measurement for scientific and industrial applications.