Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys.

Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium or Galinstan are high-surface tension fluids coated with a ∼5 nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LMs). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning (ML) technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from ML and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems.

Reactive etching of gallium oxide on eutectic gallium indium (eGaIn) with chlorosilane vapor to induce differential wetting.

Differentially wettable surfaces are well sought after in energy, water, health care, separation science, self-cleaning, biology, and other lab-on-chip applications-however, most demonstrations of realizing differential wettability demand complex processes. Herein, we chemically etch gallium oxide (Ga2O3) from in-plane patterns (2D) of eutectic gallium indium (eGaIn) to demonstrate a differentially wettable interface using chlorosilane vapor. We produce 2D patterns of eGaIn on bare glass slides in native air using cotton swabs as paint brushes. Exposing the entire system to chlorosilane vapor induces chemical etching of the oxide layer, which recovers the high-surface energy of eGaIn, to produce nano-to-mm droplets on the pre-patterned area. We rinse the entire system with deionized (DI) water to achieve differentially wettable surfaces. Measurements of contact angles using a goniometer confirmed hydrophobic and hydrophilic interfaces. Scanning electron microscopy (SEM) images confirmed the distribution and energy dispersive spectra (EDS) exhibited the elemental compositions of the micro-to-nano droplets after silanization (silane treatment). Also, we demonstrated two proofs of concept, i.e., open-ended microfluidics and differential wettability on curved interfaces, to demonstrate the advanced applications of the current work. This straightforward approach using two soft materials (silane and eGaIn) to achieve differential wettability on laboratory-grade glass slides and other surfaces has future implications for nature-inspired self-cleaning surfaces in nanotechnologies, bioinspired and biomimetic open-channel microfluidics, coatings, and fluid-structure interactions.