Iakovlev Ilia A, Deviatov Alexander Y, Lvov Yuri, Fakhrullina Gölnur, Fakhrullin Rawil F, Mazurenko Vladimir V
Theoretical Physics and Applied Mathematics Department, Ural Federal University, Mira Street 19, Ekaterinburg 620002, Russia.
Institute for Micromanufacturing, Louisiana Tech University, Ruston, Louisiana 71272, United States.
ACS Nano. 2022 Apr 26;16(4):5867-5873. doi: 10.1021/acsnano.1c11025. Epub 2022 Mar 29.
Reproducibility of the experimental results and object of study itself is one of the basic principles in science. But what if the object characterized by technologically important properties is natural and cannot be artificially reproduced one-to-one in the laboratory? The situation becomes even more complicated when we are interested in exploring stochastic properties of a natural system and only a limited set of noisy experimental data is available. In this paper we address these problems by exploring diffusive motion of some natural clays, halloysite and sepiolite, in a liquid environment. By using a combination of dark-field microscopy and machine learning algorithms, a quantitative theoretical characterization of the nanotubes' rotational diffusive dynamics is performed. Scanning the experimental video with the gradient boosting tree method, we can trace time dependence of the diffusion coefficient and probe different regimes of nonequilibrium rotational dynamics that are due to contacts with surfaces and other experimental imperfections. The method we propose is of general nature and can be applied to explore diffusive dynamics of various biological systems in real time.
实验结果的可重复性以及研究对象本身是科学的基本原则之一。但是，如果具有技术重要属性的研究对象是天然的，且无法在实验室中一对一地人工重现，会怎样呢？当我们对探索自然系统的随机属性感兴趣，且仅有一组有限的含噪声实验数据可用时，情况会变得更加复杂。在本文中，我们通过研究一些天然黏土（埃洛石和海泡石）在液体环境中的扩散运动来解决这些问题。通过结合暗场显微镜和机器学习算法，对纳米管的旋转扩散动力学进行了定量理论表征。使用梯度提升树方法扫描实验视频，我们可以追踪扩散系数的时间依赖性，并探究由于与表面接触和其他实验缺陷导致的非平衡旋转动力学的不同状态。我们提出的方法具有通用性，可应用于实时探索各种生物系统的扩散动力学。
