Alexander Alexeev

ABSTRACT Polyacrylamide hydrogels with depth‐wise gradients in polymer density (i.e., surface gel layers) are ideal synthetic models to understand stress modulation in hierarchical, compositionally‐graded biological tissues, including articular cartilage, in part, due to their similarities in water content and network structure. This work investigated surface gel layer thickness and crosslinker mobility (e.g., covalent crosslinks vs. physical entanglements) in polyacrylamide hydrogels and their impact on mechanical properties via confocal microscopy, indentation and compression measurements, neutron reflectivity, and mesoscale modeling. Hydrogels polymerized against oxygen‐permeable polydimethylsiloxane exhibited thicker surface gel layers and significantly lower elastic modulus compared to hydrogels polymerized against oxygen‐impermeable glass. Physical entanglements lowered the hydrogel elastic modulus within the surface gel layer and throughout the bulk. Neutron reflectivity revealed the collapse of near‐surface polymer networks under compressive loads, in good agreement with dissipative particle dynamics (DPD) simulations. Our results suggested that both the hydrogel elastic modulus and crosslinker mobility set the load required to collapse the polymer network. Furthermore, the collapse of thicker surface gel layers resulted in lower polymer network density than thinner surface gel layers. This work points to polymer density and crosslinker mobility as key design parameters in the design of stress‐modulating and tissue‐like materials.

Abstract Heat transfer involving phase change is computationally intensive due to moving phase boundaries, nonlinear computations, and time step restrictions. This paper presents a quantum lattice Boltzmann method (QLBM) for simulating heat transfer with phase change. The approach leverages the statistical nature of the lattice Boltzmann method (LBM) while addressing the challenges of discontinuous phase transitions in quantum computing. The method implements an interface-tracking strategy that partitions the problem into separate solid and liquid domains, enabling the algorithm to handle the discontinuity in the enthalpy–temperature relationship. We store phase change information in the quantum circuit to reduce information exchange between classical and quantum hardware, a bottleneck in many quantum applications. Results from the implementation agree with both classical LBM and analytical solutions, demonstrating QLBM as an effective approach for analyzing thermal systems with phase transitions. Simulations using 17 lattice nodes with 53 qubits demonstrate temperature root-mean-square errors of order 0.01 when compared against classical solutions. The method accurately tracks interface movement during phase transition.

The hydrodynamic performance of oscillating elastic plates with tapered and uniform thickness in an incompressible Newtonian fluid at varying Reynolds numbers is investigated numerically using a fully coupled fluid–structure interaction computational model. By leveraging the acoustic black hole effect, tapered plates can generate bending patterns that vary from standing wave to travelling wave oscillations, whereas plates with uniform thickness are limited to standing wave oscillations. Simulations reveal that although both standing and traveling wave oscillation modes can produce high thrust, travelling waves achieve significantly higher hydrodynamic efficiency, and this advantage is more pronounced at higher Reynolds numbers. Furthermore, regardless of the oscillation mode, tapering leads to greater hydrodynamic performance. The enhanced hydrodynamic efficiency of travelling wave propulsion is associated with the reduced amount of vorticity generated by tapered plates, while maintaining high tip displacements. The results have implications for the development of highly efficient biomimetic robotic swimmers, and more generally, the better understanding of the undulatory aquatic locomotion.

High Resolution Image Download MS PowerPoint Slide Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number ( Wi ). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi . Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

Changes in the mechanical properties, i.e., mechanotypes, of tissues are powerful indicators of disease states and drug-induced injuries. Although differential mechanotyping has emerged as a valuable tool for non-invasive disease diagnostics, it remains particularly underutilized for drug safety and efficacy screening in preclinical studies. This is largely due to the lack of scalable mechanotyping methods compatible with modern 3D organoid models. Here, the Centrifugal Mechanical Testing (CeMeT) platform is presented, which enables rapid, robust, and label-free mechanotyping of 3D organoids. Utilizing centrifugal mechanical principles and high-speed imaging, this platform achieves high accuracy and precision and can assess a wide range of tissue stiffness. It is demonstrated that the CeMeT platform distinguishes mechanical properties, i.e., stiffness and elastic recovery, among various hydrogel bead formulations and hiPSC-derived cardiac organoids, successfully detecting pathological changes in mechanotype with high sensitivity. Through experiments on organoids treated with drugs like pergolide and Cytochalasin-D, it is established that changes in organoid mechanotypes can serve as reliable indicators of drug-induced tissue injuries in vitro. These findings position the CeMeT platform as a potentially transformative tool for early-stage drug safety assessment through mechanotyping, with immediate applications extending to fundamental disease pathology research and drug efficacy testing using organoid models.

Fluid–structure interactions (FSI) underpin diverse biological and engineering systems, yet computational modeling of these interactions remains resource-intensive and constrained by high-dimensional parameter spaces. To address these limitations, we develop a data-driven, two-stage surrogate modeling framework leveraging Fourier neural operators (FNO) to accurately predict the hydrodynamic performance of biomimetic elastic propulsors across a broad range of design parameters. Our approach first maps geometric and physical parameters to the propulsor dynamic bending pattern and then uses this bending pattern to predict thrust, power, and efficiency. Trained using direct FSI simulation data, our surrogate model achieves three orders of magnitude speedup compared to the direct simulations while maintaining high accuracy. Crucially, the use of intermediate kinematic modeling significantly reduces the overall model size and enhances accuracy. Our FNO modeling framework enables rapid exploration of large design spaces, indicating strong potential for application in the design and optimization of FSI problems.

This study investigates the impact of MoSe2 nanoparticle doping on the structural, optical, and electricalproperties of the ZnO electron transport layer (ETL), as well as its effect on the efficiency of organic solarcells (OSCs). ZnO:MoSe2 composites were synthesized using the sol-gel method, and their morphology wasanalyzed by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Optical studiesrevealed an increase in the bandgap width and enhanced defect-related emission, indicating improvedcharge carrier dynamics. Electrical measurements confirmed increased conductivity and reduced charge recombinationwith the addition of MoSe2. Organic solar cells based on ZnO:MoSe2 demonstrated enhancedphotovoltaic performance compared to pure ZnO devices. The optimal device was achieved at MoSe2 concentrationof 8 %, where the short-circuit current density (Jsc) increased from 7.25 mA/cm² to 10.02 mA/cm2, thefill factor (FF) improved from 0.37 to 0.52, and the power conversion efficiency (PCE) rose from 0.7 % to3.3 %. These results confirm the potential of ZnO:MoSe2 nanocomposites for high-performance optoelectronicand photovoltaic devices.

The article considers modern tasks of collecting and interpreting data on the state of technical equipment, prospects for integrating various types of sensors and protocols into a single information network within the framework of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies, as well as features of using predictive analytics methods to ensure operating regulations based on the actual state. The key technical difficulties associated with differences in polling speed, data volumes and protocol incompatibility are presented. The developed Pulse protocol and the Signalogic platform are described, providing efficient data processing, process automation and the ability to create a digital layer of the enterprise. The results of comparative tests of the performance of the Pulse protocol with MQTT are presented, demonstrating its superiority in signal processing speed. Examples of successful application of the developed solutions in industry are considered, including control systems for mining and processing equipment, as well as hazardous waste disposal. Particular attention is paid to the potential of predictive analytics in IoT and IIoT for predicting malfunctions, optimizing production processes and increasing equipment reliability. The paper describes the prospects for using machine learning technologies, real-time data analysis, and virtual stands for developing and testing systems. The paper highlights the importance of an interdisciplinary approach for the further development of IoT integration technologies that combine analytical, UX design, and engineering solutions.

The article describes some approaches to a reduce of production costs of agricultural producers.The main sources of information were: the results of experimental investigations, carried off in Verkhnevolzhsky Federal Agrarian Research Centre (Russia) in the field crop rotation; production and finance documentation of "Rassvet" APC (Ivanovo region) and "Suvorovskoe" JSC (Vladimir region); photochronographic observations, conducted at some agricultural organizations.In the work the follow methods were used: system and situate approaches, methods of comparative analysis, analysis of breakeven, method of net cost calculation "Direct -costing".The main approaches are: the use of scale effect and production diversification; choice of rational agricultural system, production intensification level, system of machines, fertilizer so on; choice of cheaper materials, selection of the less expensive technological operations and them combinations based on situational approach, in particular, taking into account weather conditions.As well as the matter of efficiency of input costs for crops cultivation is described.

Abstract Thin liquid film flowing down the inner concave surface of a vertical cylindrical vessel is examined. At the top of the vessel, the water is injected horizontally at high speed circumferentially along the vessel wall and flows downwards due to the action of gravity. This turbulent film flow is modeled using the large eddy simulation (LES) and Reynolds averaged Navier–Stokes (RANS) approaches combined with the volume-of-fluid method. The results of both methods are validated with direct numerical simulation. The Favre-filtered two-phase LES, which is implemented and studied in this paper, can reasonably predict the film thickness similarly to that of the RANS approach using the elliptic blending Reynolds stress model, although it requires fine resolution in the wall region. The effect of volume flow rate on the film structure and thickness is investigated. The film thickness is shown to be nearly constant when the wall is partially wetted and changes as the cubic root of the volume flow rate when the spinning film encloses the entire circumference of the vessel.

Improvements in both the power and energy density of lithium-ion batteries (LIBs) will enable longer driving distances and shorter charging times for electric vehicles (EVs). The use of thicker and denser electrodes reduces LIB manufacturing costs and increases energy density characteristics at the expense of much slower Li-ion diffusion, higher ionic resistance, reduced charging rate, and lower stability. Contrary to common intuition, we unexpectedly discovered that removing a tiny amount of material (<0.4 vol %) from the commercial electrodes in the form of sparsely patterned conical pores greatly improves LIB rate performance. Our research revealed that upon commercial production of high areal capacity electrodes, a very dense layer forms on the electrode surface, which serves as a bottleneck for Li-ion transport. The formation of sparse conical pore channels overcomes such a limitation, and the facilitated ion transport delivers much higher power without reduction in the practically attainable energy. Diffusion and finite element method-based simulations provide deep insights into the fundamentals of ion transport in such electrode designs and corroborate the experimental findings. The reported insights provide a major thrust to redesigning automotive LIB electrodes to produce cheaper, longer driving range EVs that retain fast charging capability.

Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based mesoscale numerical methods can be executed as efficient quantum algorithms due to their statistical features. This approach revises a quantum algorithm for lattice gas automata to reduce classical computations and state preparation at every time step. For this, the algorithm approximates the qubit relative phases and subtracts them at the end of each time step. Phases are evaluated using the iterative phase estimation algorithm and subtracted using single-qubit rotation phase gates. This method optimizes the quantum resource required and makes it more appropriate for near-term quantum hardware. We also demonstrate how the checkerboard deficiency that the D1Q2 scheme presents can be resolved using the D1Q3 scheme. The algorithm is validated by simulating two canonical partial differential equations: the diffusion and Burgers' equations on different quantum simulators. We find good agreement between quantum simulations and classical solutions for the presented algorithm.

Visual sensors play an important role in a broad variety of robotic systems applications.Even though Kinect technology appeared over 10 years ago, Kinect sensors are still actively employed by researchers around the world.This paper presents an overview of Kinect and Kinect 2 sensors' applications in a human gesture based control.We analyzed existing research papers to estimate a popularity of particular feature extraction and gesture recognition methods, recommendations on a distance between an object of interest and a sensor, reported accuracy and latency of the sensor.Our analysis is supposed to facilitate a selection of a suitable combination of methods for a particular application of Kinect sensor in gesture recognition while considering its performance.

This work proposes a multi-circuit quantum lattice Boltzmann method (QLBM) algorithm that leverages parallel quantum computing to reduce quantum resource requirements. Computational fluid dynamics (CFD) simulations often entail a large computational burden on classical computers. At present, these simulations can require up to trillions of grid points and millions of time steps. To reduce costs, novel architectures like quantum computers may be intrinsically more efficient for these computations. Current quantum algorithms for solving CFD problems are based on single quantum circuits and, in many cases, use lattice-based methods. Current quantum devices are adorned with sufficient noise to make large and deep circuits untenable. We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solve of the incompressible Navier--Stokes equations. The method, called QLBM-frugal, aims to create more practical quantum circuits and strategies for differential equation-based problems. The presented method is validated and demonstrated for 2D lid-driven cavity flow. The two-circuit algorithm shows a marked reduction in CNOT gates, which consume the majority of the runtime on quantum devices. Compared to the baseline QLBM technique, a two-circuit strategy shows increasingly large improvements in gate counts as the qubit size, or problem size, increases. For 64 lattice sites, the CNOT count was reduced by 35%, and the gate depth decreased by 16%. This strategy also enables concurrent circuit execution, further halving the seen gate depth.

Numerous applications in medical diagnostics, cell engineering therapy, and biotechnology require the identification and sorting of cells that express desired molecular surface markers. We developed a microfluidic method for high-throughput and label-free sorting of biological cells by their affinity of molecular surface markers to target ligands. Our approach consists of a microfluidic channel decorated with periodic skewed ridges and coated with adhesive molecules. The periodic ridges form gaps with the opposing channel wall that are smaller than the cell diameter, thereby ensuring cell contact with the adhesive surfaces. Using three-dimensional computer simulations, we examine trajectories of adhesive cells in the ridged microchannels. The simulations reveal that cell trajectories are sensitive to the cell adhesion strength. Thus, the differential cell trajectories can be leveraged for adhesion-based cell separation. We probe the effect of cell elasticity on the adhesion-based sorting and show that cell elasticity can be utilized to enhance the resolution of the sorting. Furthermore, we investigate how the microchannel ridge angle can be tuned to achieve an efficient adhesion-based sorting of cells with different compliance.

The separation of peripheral blood mononuclear cells (PBMCs) into constituent blood cell types is a vital step to obtain immune cells for autologous cell therapies. The ability to separate PBMCs using label-free microfluidic techniques, based on differences in biomechanical properties, can have a number of benefits over other conventional techniques, including lower cost, ease of use, and avoidance of animal-derived labeling antibodies. Here, we report a microfluidic device that uses compressive diagonal ridges to separate PBMCs into highly pure samples of viable and functional lymphocytes. The technique utilizes the differences in the biophysical properties of PBMC sub-populations to direct the lymphocytes and monocytes into separate outlets. The biophysical properties of the monocytes and lymphocytes from healthy donors were first characterized using atomic force microscopy. Lymphocytes were found to be significantly stiffer than monocytes, with a mean cell stiffness of 1495 and 931 Pa, respectively. The differences in biophysical properties resulted in distinct trajectories through the microchannel terminating at different outlets, resulting in a lymphocyte sample with purity and viability both greater than 96% with no effect on the cells' ability to produce interferon gamma, a cytokine crucial for innate and adaptive immunity.

Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based mesoscale numerical methods can be executed as efficient quantum algorithms due to their statistical features. This approach revises a quantum algorithm for lattice gas automata to reduce classical computations and state preparation at every time step. For this, the algorithm approximates the qubit relative phases and subtracts them at the end of each time step. Phases are evaluated using the iterative phase estimation algorithm and subtracted using single-qubit rotation phase gates. This method optimizes the quantum resource required and makes it more appropriate for near-term quantum hardware. We also demonstrate how the checkerboard deficiency that the D1Q2 scheme presents can be resolved using the D1Q3 scheme. The algorithm is validated by simulating two canonical PDEs: the diffusion and Burgers' equations on different quantum simulators. We find good agreement between quantum simulations and classical solutions for the presented algorithm.

PEDOT:PSS is one of the most widely used materials as a hole selective layer in organic photovoltaics due to its easy processing and high reproducibility. Unfortunately, the material is limited when testing new donor:acceptor systems due to its intrinsic frontier energy levels which typically leads to energy losses due to inadequate energy level alignment and presence of resistive losses. In this work, PEDOT:PSS:metal phthalocyanines nanocomposite thin films are formulated and used as hole transport layer for organic solar cells (OSCs). PEDOT:PSS is formulated with H2Pc, CuPc, CoPc and ZnPc metal phthalocyanines (MPc) with nanobelt morphology which confers the compatibility with the active layer. Atomic force microscopy (AFM) and x-ray diffraction (XRD) were used to study the morphology and structure of nanocomposite films, respectively. OSCs based on PEDOT:PSS:MPc nanocomposite films were fabricated and the effect of hybrid hole transport layer with various phthalocyanines on photovoltaics properties was studied. Overall, nanocomposition of PEDOT:PSS with metal phthalocyanines improves the final power conversion efficiency of solar cells by 20% by a reduction of the resistive losses due to inadequate energy level alignment. The addition of metal phthalocyanines to PEDOT:PSS is a promising method for tailor-made hole transport materials for new donor:acceptor systems to improve their efficiencies.

Sorting biological cells in heterogeneous cell populations is a critical task required in a variety of biomedical applications and therapeutics. Microfluidic methods are a promising pathway toward establishing label-free sorting based on cell intrinsic biophysical properties, such as cell size and compliance. Experiments and numerical studies show that microchannels decorated with diagonal ridges can be used to separate cell by stiffness in a Newtonian fluid. Here, we use computational modeling to probe stiffness-based cell sorting in ridged microchannels with a power-law shear thinning fluid. We consider compliant cells with a range of elasticities and examine the effects of ridge geometry on cell trajectories in microchannel with shear thinning fluid. The results reveal that shear thinning fluids can significantly enhance the resolution of stiffness-based cell sorting compared to Newtonian fluids. We explain the mechanism leading to the enhanced sorting in terms of hydrodynamic forces acting on cells during their interactions with the microchannel ridges.

Peristaltic fluid pumping due to a periodically propagating contraction wave in a vessel fitted with one-way elastic valves is investigated numerically. It is concluded that the valve spacing within the vessel relative to the contraction wavelength plays a critical role in providing efficient pumping. When the valve spacing does not match the wavelength, the valves open asynchronously and the volume of the vessel segments bounded by two consecutive valves changes periodically, thereby inducing volumetric fluid pumping. The volumetric pumping leads to higher pumping flowrate and efficiency against an adverse pressure gradient. The optimum pumping occurs when the ratio of valve spacing to contraction wavelength is about2/3. This pumping regime is characterized by a longer period during which the valves are open. The results are useful for further understanding the pumping features of lymphatic system and provide insight into the design of biomimetic pumping devices.

Structural optimization of butyrylcholinesterase inhibitors, 5-bromomethyl- and 5-iodomethyl- N , N -disubstituted 2-aminothiazolines, led to a series of their annulated bicyclic analogues, obtained by intramolecular cyclization of cycloalkenylthioureas. The most active compound in this series, cyclohepta[ d ]thiazol-2-amine, is a mixed-type butyryl-cholinesterase inhibitor with IC 50 = 130 nm, highly selective compared to acetylcholinesterase and non-toxic at 100 μm concentrations .