Abstract Wettability characterizations are crucial to many disciplines, including dropwise condensation heat transfer, microfluidics, and self‐cleaning materials. Droplet goniometry is a standard technique for such characterizations due to its relatively simple setup and execution. However, goniometry is severely limited when analyzing irregular droplets and multi‐droplet systems, and alternative methods often require additional equipment, exhaustive scans, or non‐physics‐based modeling, hindering their effectiveness. In response, we created the reverse catch light method, which constructs physics‐informed, three‐dimensional digital twins of droplets from a single overhead image. We locate droplet contact lines and point light reflections on a real droplet surface, then iteratively solve the Young‒Laplace equation to generate candidate droplet surfaces whose unique reflections match those observed in our image. We experimentally validated this method with goniometry across varied droplet contact angles, volumes, liquid types, and substrates. Furthermore, we highlight new capabilities that are impractical with classical goniometry, such as constructing digital twins of irregularly shaped droplets or sliding droplets for hysteresis characterization. Finally, we demonstrate the ability to obtain rich, spatiotemporal statistical data from multi‐droplet systems that would be impossible with classical goniometry. Our findings suggest that this methodology could be easily deployed for enhanced in situ diagnostics of otherwise difficult‐to‐analyze, realistic systems.
Berk et al. (Wed,) studied this question.