Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys.
Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys.
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DOI：
10.1088/2515-7639/acf78c
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发表时间：
2023-10-01
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氧化物涂层的高表面张力流体的悬垂液滴经常产生干扰的形状，阻碍界面的研究。铕镓铟或Galinstan是涂覆有105 nm氧化镓（Ga 2 O3）膜的高表面张力流体，并且福尔斯属于该流体分类，也称为液态金属（LM）。最近出现的LM为基础的应用程序往往不能进行没有分析在不同的环境中的界面能量。虽然在文献中有许多技术可用于界面研究，但基于悬垂液滴的分析是最简单的。然而，由于表面氧化物的存在，悬垂液滴的扰动形状经常被忽略，作为误差的来源。此外，利用振荡悬垂液滴的表面氧化物的探索性研究仍然没有开发。我们解决这两个挑战，并提出了两个贡献的新颖性-（一）通过利用机器学习（ML）技术，我们预测的扰动悬垂液滴的近似表面张力值，（ii）通过利用振荡诱导的气泡张力法，我们研究的氧化物涂层LM液滴的动态弹性模量。我们已经从LM的悬垂液滴形状参数创建了我们的数据集，并训练了不同的模型进行比较。我们在所有型号上都实现了>99%的准确性，并增加了与其他流体一起工作的多功能性。最佳性能的模型被进一步利用来预测非轴对称LM液滴的近似值。然后，我们分析了LM在空气中的弹性和粘性模量，利用振荡诱导的悬垂液滴，这为替代昂贵的流变仪的界面研究提供了补充机会。我们相信这将使更多的基础研究的氧化层LM，利用对称和扰动液滴。我们的研究拓宽了材料科学的视野，来自ML和人工智能领域的研究人员可以协同工作，解决与表面科学、界面研究和其他与基于LM的系统相关的研究相关的更复杂的问题。
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.
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