ACS Nano, volume 16, issue 4, pages 5867-5873
Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning
Iakovlev Ilia A.
1
,
Deviatov Alexander Y
1
,
Lvov Y.
2
,
Fakhrullina Gölnur
3
,
Fakhrullin R.F.
3
,
MAZURENKO V. V.
1
2
Institute for Micromanufacturing, Louisiana Tech University, Ruston, Louisiana 71272, United States
|
Publication type: Journal Article
Publication date: 2022-03-29
General Physics and Astronomy
General Materials Science
General Engineering
Abstract
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.
Citations by journals
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1
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Bulletin of the Chemical Society of Japan
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Bulletin of the Chemical Society of Japan
1 publication, 8.33%
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Molecules
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Molecules
1 publication, 8.33%
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Particle and Fibre Toxicology
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Particle and Fibre Toxicology
1 publication, 8.33%
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Composites Science and Technology
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1 publication, 8.33%
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Chemical Engineering Journal Advances
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Chemical Engineering Journal Advances
1 publication, 8.33%
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International Journal of Biological Macromolecules
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International Journal of Biological Macromolecules
1 publication, 8.33%
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ACS Omega
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ACS Omega
1 publication, 8.33%
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Physical Review A
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Physical Review A
1 publication, 8.33%
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Physical Review E
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Physical Review E
1 publication, 8.33%
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Advanced Membranes
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Advanced Membranes
1 publication, 8.33%
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Advanced Sustainable Systems
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Advanced Sustainable Systems
1 publication, 8.33%
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Lobachevskii Journal of Mathematics
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Lobachevskii Journal of Mathematics
1 publication, 8.33%
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1
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Citations by publishers
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1
2
3
4
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Elsevier
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Elsevier
4 publications, 33.33%
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American Physical Society (APS)
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American Physical Society (APS)
2 publications, 16.67%
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The Chemical Society of Japan
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The Chemical Society of Japan
1 publication, 8.33%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 8.33%
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Springer Nature
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Springer Nature
1 publication, 8.33%
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American Chemical Society (ACS)
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American Chemical Society (ACS)
1 publication, 8.33%
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Wiley
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Wiley
1 publication, 8.33%
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Pleiades Publishing
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Pleiades Publishing
1 publication, 8.33%
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4
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Iakovlev I. A. et al. Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning // ACS Nano. 2022. Vol. 16. No. 4. pp. 5867-5873.
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Iakovlev I. A., Deviatov A. Y., Lvov Y., Fakhrullina G., Fakhrullin R., MAZURENKO V. V. Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning // ACS Nano. 2022. Vol. 16. No. 4. pp. 5867-5873.
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TY - JOUR
DO - 10.1021/acsnano.1c11025
UR - https://doi.org/10.1021%2Facsnano.1c11025
TI - Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning
T2 - ACS Nano
AU - Deviatov, Alexander Y
AU - Fakhrullina, Gölnur
AU - Iakovlev, Ilia A.
AU - Lvov, Y.
AU - Fakhrullin, R.F.
AU - MAZURENKO, V. V.
PY - 2022
DA - 2022/03/29 00:00:00
PB - American Chemical Society (ACS)
SP - 5867-5873
IS - 4
VL - 16
SN - 1936-0851
SN - 1936-086X
ER -
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@article{2022_Iakovlev,
author = {Alexander Y Deviatov and Gölnur Fakhrullina and Ilia A. Iakovlev and Y. Lvov and R.F. Fakhrullin and V. V. MAZURENKO},
title = {Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning},
journal = {ACS Nano},
year = {2022},
volume = {16},
publisher = {American Chemical Society (ACS)},
month = {mar},
url = {https://doi.org/10.1021%2Facsnano.1c11025},
number = {4},
pages = {5867--5873},
doi = {10.1021/acsnano.1c11025}
}
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MLA
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Iakovlev, Ilia A., et al. “Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning.” ACS Nano, vol. 16, no. 4, Mar. 2022, pp. 5867-5873. https://doi.org/10.1021%2Facsnano.1c11025.