Michael Nosonovsky

We investigate the spontaneous rearrangement of microdroplets in a self-assembled droplet cluster levitating over a thin locally heated water layer. The center-to-periphery droplet diameter ratio (the “inversion coefficient”) controls the onset of the inversion. Larger droplets can squeeze between smaller ones due to increased drag force on them from the air–vapor flow. In smaller clusters, the rotation of the droplets plays an important role since larger droplets rotating with the same angular velocity (dependent on the rotor of the airflow field) have higher viscous friction force with the liquid layer. It is desirable to avoid cluster inversion in experiments where individual droplet positions should be traced.

Surface of polyhydroxyalkanoate (PHA) films of varying monomer compositions are analyzed using atomic force microscopy (AFM) and unsupervised machine learning (ML) algorithms to investigate and classify films based on global attributes such as the scan size, film thickness, and monomer type. The experiment provides benchmarked results for 12 of the most widely used clustering algorithms via a hybrid investigation approach while highlighting the impact of using the Fourier transform (FT) on high-dimensional vectorized data for classification on various pools of data. Our findings indicate that the use of a one-dimensional (1D) FT of vectorized data produces the most accurate outcome. The experiment also provides insights into case-by-case investigations of algorithm performances and the impact of various data pools. Lastly, we show an early version of our tool aimed at investigating surfaces using ML …

The study of friction is traditionally a data-driven area with many experimental data and phenomenological models governing structure-property relationships. Triboinformatics is a new area combining Tribology with Machine Learning (ML) and Artificial Intelligence (AI) methods, which can help to establish correlations in data on friction and wear. This is particularly relevant to unstable motion, where deterministic models are difficult to build. There are several types of friction-induced instabilities including those caused by the velocity dependency of dry friction, coupling of friction with another process (wear, heat generation, etc.), the elastic Adams instabilities, and others. The onset of sliding is also an unstable process. ML/AI methods, such as Topological Data Analysis and various ML algorithms, which have been already used for various aspects of data analysis on friction, can be applied also to the frictional instabilities.

We develop new computer vision method of automatic image processing of cavitation bubbles to classify petroleum products with different octane numbers (ON) using an artificial neural network (ANN). Ultrasonic irradiation induces cavitation bubbles, which exhibit growth, oscillations, and resonance shapes. Gasoline solutions may have different physical and chemical properties. While precise understanding of how these properties impacts bubble dynamics is challenging, training ANN algorithm on bubble images allows classification of gasoline bubbles with different ON values. The integration of ultrasonic cavitation method with computer vision and artificial intelligence techniques offers a promising way for real-time ON assessment in liquid flow.

The observation of the amazing life of small water droplets and their bizarre collective behavior is not only a fascinating experience. Such phenomena, although invisible to the human eye, determine large-scale physical processes in the Earth’s atmosphere and in the surface layer of the world ocean. This is enough to understand the continuing interest of researchers, who for many years have been working to understand the physical laws of what at first was a mere wonder. Due to circumstances, the authors of this monograph got involved in a particular but very interesting problem related to the self-arrangement of regular structures from micron-sized droplets levitating over the locally heated water surface. The joint experimental and theoretical work has generated a number of original results, which have been published in leading international journals. To date, we have not only begun to understand some features …

This paper reviews the synthesis, characterization, healing assessment, and mechanics of NiTi and other shape memory alloy (SMA)-reinforced self-healing metal matrix composites (SHMMCs). Challenges to synthesizing and characterizing the SMA-reinforced SHMMCs and the strategies followed to overcome those challenges are discussed. To design the SMA-reinforced SHMMCs, it is necessary to understand their microstructural evolution during melting and solidification. This requires the knowledge of the thermodynamics of phase diagrams and nonequilibrium solidification, which are presented in this paper for a model self-healing composite system. Healing assessment provides information about the autonomous and multicycle healing capability of synthesized SHMMCs, which ultimately determines their success. Different techniques to assess the degree of healing of SHMMCs are discussed in this paper. Strategies are explored to find the optimum volume fraction of SMA wires needed to yield the matrix and prevent damage to the SMA wires for the most effective healing. Finally, major challenges, knowledge gaps, and future research directions, including the need for autonomous and multicycle healing capability in SMA-reinforced SHMMCs, are outlined.

We investigate the correlation between the Voronoi entropy (VE) of ligand molecules and their affinity to receptors to test the hypothesis that less ordered ligands have higher mobility of molecular groups and therefore a higher probability of attaching to receptors. VE of 1144 ligands is calculated using SMILES-based 2D graphs representing the molecular structure. The affinity of the ligands with the SARS-CoV-2 main protease is obtained from the BindingDB Database as half-maximal inhibitory concentration (IC50) data. The VE distribution is close to the Gaussian, 0.4 ≤ Sv ≤ 1.66, and a strong correlation with IC50 is found, IC50 = −275 Sv + 613 nM, indicating the correlation between ligand complexity and affinity. On the contrary, the Shannon entropy (SE) descriptor failed to provide enough evidence to reject the null hypothesis (p-value > 0.05), indicating that the spatial arrangement of atoms is crucial for …

Clusters of nearly identical water microdroplets levitating over a locally heated water layer are considered. The high-resolution and high-speed fluorescence microscopy showed that there is a universal brightness profile of single droplets, and this profile does not depend on the droplet temperature and size. We explain this universal profile using the theory of light scattering and propose a new method for determining the parameters of possible optical inhomogeneity of a droplet from its fluorescent image. In particular, we report for the first time and explain the anomalous fluorescence of some large droplets with initially high brightness at the periphery of the droplet. The disappearance of this effect after a few seconds is related to the diffusion of the fluorescent substance in water. Understanding the fluorescence profiles paves the way for the application of droplet clusters to the laboratory study of biochemical …