This review focuses on surface modifications to gallium‐based liquid metals (LMs), which are stretchable conductors with metallic conductivity and nearly unlimited extensibility due to their liquid nature. Despite the enormous surface tension of LM, it can be patterned into nonspherical shapes, such as wires, due to the presence of a native oxide shell. Incorporating inherently soft LM into elastomeric devices offers comfort, mechanical compliance, and stretchability. The thin oxide layer also enables the formation of stable liquid colloids and LM micro/nanosized droplets that do not coalesce easily. The oxide layer can also be exfoliated and chemically modified into semiconductor 2D materials to create and deposit atomically thin materials at room temperature. Thus, the interface and its manipulation are important. This review summarizes physical and chemical methods of modifying the surface of LM to tune its properties. The surface modification of LM provides unique applications, including use in soft biomedical sensors and actuators with mechanical properties similar to human tissue.
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.
more » « less- NSF-PAR ID:
- 10470559
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Physics: Materials
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2515-7639
- Format(s):
- Medium: X Size: Article No. 045009
- Size(s):
- Article No. 045009
- Sponsoring Org:
- National Science Foundation
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