Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.

The rotational dynamics of microparticles in liquids have a wide range of applications, including chemical microreactors, biotechnologies, microfluidic devices, tunable heat and mass transfer, and fundamental understanding of chiral active soft matter which refers to systems composed of particles that exhibit a handedness in their rotation, breaking mirror symmetry at the microscopic level. Here, we report on the study of two effects in colloids in rotating electric fields: (i) the rotation of individual colloidal particles in rotating electric field and related to that (ii) precession of pairs of particles. We show that the mechanism responsible for the rotation of individual particles is related to the time lag between the external field applied to the particle and the particle polarization. Using numerical simulations and experiments with silica particles in a water-based solvent, we prove that the observed rotation of particle pairs and triplets is governed by the tunable rotation of individual particles and can be explained and described by the action of hydrodynamic forces. Our findings demonstrate that colloidal suspensions in rotating electric fields, under some conditions, represent a novel class of chiral soft active matter—tunable colloidal spinners. The experiments and the corresponding theoretical framework we developed open novel prospects for future studies of these systems and for their potential applications.

Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.

Tunable self-assembly driven by external electric or magnetic fields is of significant interest in modern soft matter physics. While extensively studied in two-dimensional systems, it remains insufficiently explored in three-dimensional systems. In this study, we investigated the formation of vertical strings from an initial monolayer system of particles deposited on a horizontal substrate under the influence of an external magnetic field using experiments, computer simulations, and theoretical frameworks. We demonstrated that the main mechanism of string self-assembly is merging, driven by the interplay between gravity and induced tunable interparticle interactions. During this process, the system has to overcome a saddle point on the energy landscape, whose height increases with the string height. At a certain point, further self-assembly becomes kinetically blocked in a metastable state, far from equilibrium. This contrasts sharply with the typical scenario for tunable self-assembly in two dimensions, where the resulting structures usually correspond to the equilibrium state. Therefore, this finding opens up opportunities for more detailed control of three-dimensional tunable self-assembly by designing and tuning various potential barriers along the kinetic pathways.