Technical Physics Letters
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- 2025
- 2024
- 2023
- 2022
A deep learning approach to the study of colloidal microparticles assembly process in three-dimensional space
Simkin I.V.
1, Shirokova A.A.
1, Shvetsov A.I.1, Kohanovskaya A.V.1, Zabavina P.A.1, Bondareva A.A.1, Libet P.A.
1, Kryuchkov N.P.
1, Yakovlev E.V.
1
1Bauman Moscow State Technical University, Moscow, Russia
Email: vanyasimkin@gmail.com, shirokova2001@yandex.ru, shvetsov.anton@mail.ru, libetpa@gmail.com, kruchkov_nkt@mail.ru, yakov.egor@gmail.com
We present a deep learning-based approach to visualize and analyze the three-dimensional self-assembly of microparticles in a fluid using laser-plane microscopy, followed by coordinate reconstruction in three dimensions. A YOLOv8 neural network was employed for this purpose. It has been demonstrated that the proposed post-processing technique allows for the detection of characteristic features in the scattering pattern of microparticles with a mean average precision of 0.93, as well as the extraction of their coordinates in three-dimensional space with an accuracy of up to 20% of their diameter. This approach holds promise for controlling the self-assembly processes of microparticles in three dimensions and has the potential for enhancing the development of novel materials and technologies, such as three-dimensional bioprinting and micro- and nanoscale fabrication. Keywords: soft matter, colloids, three-dimensional self-assembly, plane microscopy.
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