Probing Diffusive Dynamics of Natural Tubule Nanoclays with Machine Learning

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.

Impact of Increased Sonication-Induced Dispersion of Sepiolite on Its Interaction with Biological Macromolecules and Toxicity/Proliferation in Human Cells

“…AFM images of sepiolite nanoclays have shown prominent size distribution and varied morphology . The AFM analysis performed here (Figure ) confirmed (i) the presence of aggregates in the nonsonicated initial sample of sepiolite and the dissociation of sepiolite aggregates upon sonication.…”

“…Of note, a recent study reports on the colloidal dispersion behavior of individual sepiolite fibers by analyzing the diffusive motion of some natural clays. The authors show that sepiolite nanoclay demonstrates rich Brownian-type rotational dynamics . To improve the dispersibility of sepiolite, several strategies have been evaluated, and the most common approaches include mechanical treatment, addition of chemical dispersants to the suspension, and chemical modification of the mineral surface .…”

“…The authors show that sepiolite nanoclay demonstrates rich Brownian-type rotational dynamics. 30 To improve the dispersibility of sepiolite, several strategies have been evaluated, and the most common approaches include mechanical treatment, addition of chemical dispersants to the suspension, and chemical modification of the mineral surface. 28 Chemical treatments can modify the chemical properties of sepiolite and/or its toxicity in mammalian cells.…”

Coronas of micro/nano plastics: a key determinant in their risk assessments

“…Among other optical microscopy techniques, hyperspectral microscopy in dark-field [258] has recently been introduced as an effective method to detect and identify nanoplastics down to 100 nm [259,260]. Supplemented by artificial intelligence [261] and machine learning [262][263][264], this technique may hold promise in spectral characterization of adlayers deposited onto microplastics. Future work is needed to evaluate the feasibility of this technology in characterization of coronas on microplastics.…”

Multivariate Imaging for Fast Evaluation of in Situ Dark Field Microscopy Hyperspectral Data

“…[14] It has become an important tool for fast probing of the resonances of new plasmonic substrates, e.g., for surface-enhanced Raman scattering (SERS), [15][16][17] tip-enhanced Raman scattering (TERS), [18] plasmon-induced magnetic resonance, [19] second-harmonic generation (SHG), [20] and hot electron generation. [21] While machine learning tools [22][23][24] and automated correction [25] can be used to process images obtained in dark field microscopy directly, it is desirable to utilize them for an assessment of the full scattering spectra, so that information on different plasmon modes and their sensitivity to changes in environment can be obtained from the full hyperspectral data. [26][27][28][29] Imaging of scattering intensity at particular wavelength [26] and introducing a color code to represent the wavelength of maximum scattering [30] resemble the 'chemical mapping' univariate in other analytical applications, such as imaging mass spectrometry, Raman or FTIR microspectroscopy.…”