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.

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.

Efficient and reproducible intracellular delivery is critical for manufacturing next generation cell therapies. Mechanoporation employs mechanical forces, including shear loading, adhesion, and compressive strain, to transiently permeabilize cell membranes and enable cargo transport. However, the influence of microsecond-scale unsteady forces and the origins of variability in delivery and viability remain insufficiently characterized. Here, we performed a parametric investigation of microfluidic mechanoporation using parallelized channel designs of varied widths to systematically modulate pre-compression shear loading and strain rates under constant volumetric flow. Narrow channels were found to promote a more uniform pre-constriction loading and compressive dynamics, leading to improved reproducibility of delivery outcomes. High-speed video analysis revealed greater cell focusing and computational fluid dynamics (CFD) confirmed higher pre-constriction shear loading rates and higher asymmetric biaxial forces prior to ridges, yielding a substantial improvement in delivery efficiency in both adherent B16F10 melanoma cells and suspended T-cells. Modulating cell-surface adhesion by adjusting surface chemistry showed that adhesive coatings slightly increase delivery efficiency at the expense of viability. Changing cell stiffness with pharmacological softening caused a decline in delivery efficiency. These trends indicate that mechanoporation outcomes are governed more strongly by the kinetics of loading dictated by fluid-driven acceleration and strain rate rather than by absolute strain or adhesion magnitude. Principal component and multivariate analyses identified two significant predictors of delivery and viability: strain rate and Basset-Boussinesq history (BBH) forces. Both predictors were consistently elevated in narrow multichannel architectures that showed higher delivery and lower viability. Together, these findings demonstrate that narrow channel designs establish a geometry-driven acceleration regime characterized by elevated strain rates and BBH forces that enhances delivery efficiency while imposing a viability tradeoff.

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 a single quantum circuit and, in many cases, use lattice-based methods. Current quantum devices are adorned with sufficient noise and make large and deep circuits untenable. We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solution 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 exhibits a notable reduction in CX gates, which account for 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 CX 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.

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.

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.

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.

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. Numerous applications in medical diagnostics, cell engineering therapy, and biotechnology require the identification and sorting of cells that express desired molecular surface markers.

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.

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.

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.

Cell-based therapy, genome editing and regenerative medicine are breakthrough technologies that require rapid and safe delivery of exogenous materials into huge populations of suspended cells. Different techniques have been devised to deliver the cargo of genetic modifying molecules to target cells that include, biochemical and physical techniques. This chapter presents a detailed discussion of various mechanoporation-based techniques for intracellular cargo delivery. In mechanoporation, the cell is deformed by mechanical forces, which creates momentary permeability. This temporary permeability facilitates diffusion or convection of macromolecules from the external fluid into the cell. With the use of these methods, a wide variety of molecules or compounds that can be dispersed in a solution can be delivered quickly and directly. Further, the chapter provides the historical background, operating principles, benefits, and drawbacks of mechanoporation processes. The chapter also discusses the advancement of new microfluidic and nanotechnological techniques along with their fabrication procedures that have enabled unprecedented levels of control over the membrane disruption process. Particular focus is placed on their applications, implementation challenges, and a discussion of potential future applications, if any. Finally, a detailed comparison of various mechanoporation-based drug delivery systems is presented. In future research on mechanoporation, there can be a focus on harnessing the distinct advantages of different intracellular delivery techniques to create multifunctional platforms capable of delivering biomolecules independently or concurrently, even under less severe operating conditions.

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.

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.

Tailored treatment of various diseases, including cancer, needs a proper design of patient-specific drugs, often requiring delivery of RNA, DNA, protein, genes and various drugs into single live cells with high viability and transfection efficiency. Rapid developments in microfluidics over the years have enabled the invention of various methods for drug delivery into cells. One of the most popular engineered techniques for cellular delivery is electroporation. It works on the principle of the cell membrane becoming permeable in response to a specific electrical pulse due to the reorganization of the structures within the cell or tissue. This technique is advantageous over other physical and chemical methods due to its easy and quick operation, higher transformation efficiency, and controllable and high throughput delivery. Due to its versatility, it is possible to perform bulk electroporation (BEP), single-cell electroporation (SCEP) and localized single-cell electroporation (LSCEP). SCEP is capable of withstanding a heterogeneous electrical field centred on a single adherent or suspended cell without impacting any nearby cells. In contrast, bulk electroporation can deliver drugs in a homogeneous electric field. On the other hand, in LSCEP, organelles and internal biochemical effects enable it to assess cell-to-cell variance accurately. This chapter presents a detailed discussion of the mechanism and various electroporation techniques, including BEP, SCEP and LSCEP. Further, the chapter is concluded with the future aspects of the electroporation technique.

Contraction of blood clots plays an important role in blood clotting, a natural process that restores hemostasis and regulates thrombosis in the body. Upon injury, a chain of events culminate in the formation of a soft plug of cells and fibrin fibers attaching to wound edges. Platelets become activated and apply contractile forces to shrink the overall clot size, modify clot structure, and mechanically stabilize the clot. Impaired blood clot contraction results in unhealthy volumetric, mechanical, and structural properties of blood clots associated with a range of severe medical conditions for patients with bleeding and thrombotic disorders. Due to the inherent mechanical complexity of blood clots and a confluence of multiple interdependent factors governing clot contraction, the mechanics and dynamics of clot contraction and the interactions with red blood cells (RBCs) remain elusive. Using an experimentally informed, physics-based mesoscale computational model, we probe the dynamic interactions among platelets, fibrin polymers, and RBCs, and examine the properties of contracted blood clots. Our simulations confirm that RBCs strongly affect clot contraction. We find that RBC retention and compaction in thrombi can be solely a result of mechanistic contraction of fibrin mesh due to platelet activity. Retention of RBCs hinders clot contraction and reduces clot contractility. Expulsion of RBCs located closer to clot outer surface results in the development of a dense fibrin shell in thrombus clots commonly observed in experiments. Our simulations identify the essential parameters and interactions that control blood clot contraction process, highlighting its dependence on platelet concentration and the initial clot size. Furthermore, our computational model can serve as a useful tool in clinically relevant studies of hemostasis and thrombosis disorders, and post thrombotic clot lysis, deformation, and breaking.

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 about 2/3 . 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.

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.