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 (GaO) 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.

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 (GaO) 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.