arXiv subjects

Ameeya Kumar Nayak

Publications and source records attributed to Ameeya Kumar Nayak.

4 recordsLinked to original sources

arXiv2026

Load-dependent Taylor dispersion in a compliant electroosmotic pump conveying a simplified Phan-Thien-Tanner fluid

We develop a coupled model for electroosmotic pumping and passive-solute dispersion of a solvent-free simplified Phan-Thien-Tanner fluid in a compliant slit microchannel. Pressure, wall deformation, axial field, velocity, and dispersion are evaluated self-consistently along the finite-throughput pump characteristic. Lubrication theory, Debye-Huckel electrostatics, an elastic-foundation wall law, and Taylor-Aris macrotransport yield a closed-form flux relation for combined electroosmotic and pressure-driven forcing. Because the shear rate depends cubically on the total shear stress, the two contributions cannot be superposed. The flux decreases monotonically with pressure gradient, ensuring a unique inversion at prescribed throughput. Current conservation couples the axial field to the deformed gap under constant-current and constant-voltage operation. In pressure-free flow, thinning the electric double layer produces a plug-like profile and the Newtonian Taylor coefficient decays as the inverse square of the Debye parameter. Under hydraulic loading, an adverse pressure gradient drives a sheared core counterflow that persists in the thin-double-layer limit, causing the coefficient to approach a finite plateau. At fixed nonzero throughput, partial cancellation between electroosmotic and pressure-driven shear yields a maximum plate number at finite double-layer thickness. This optimum is conditional: joint optimization over throughput and double-layer thickness shifts the overall optimum toward free-flow, thin-double-layer operation. Viscoelasticity can enhance or suppress loaded dispersion, while compliance shifts the pump characteristic and separation optimum. Brownian dynamics validates the reduced model. The resulting load-resolution relation identifies conditions that balance pressure delivery and separation performance.

physics.flu-dyn↗

arXiv2026

Dispersion of multiple charged species in an axially symmetric slowly varying channel

The transport and dispersion of multiple species of charged ions are central to many biological and physical processes, including electrokinetic ion separation. However, most theoretical studies of dispersion in channels have focused on neutral solutes, leaving the transport of multiple charged species comparatively unexplored. Differences in ionic diffusivities in a multispecies electrolyte solution generate an self-induced electric fields that drive electromigration. To capture these effects at the macroscopic scale, we combine the lubrication approximation with homogenization theory, under electroneutrality and zero-current constraints, to derive an effective transport equation governing the cross-sectionally averaged concentrations. We apply our model framework to a range of channel geometries and compute the resulting effective dispersion coefficients. Finally, we investigate how channel geometry can be tuned to enhance ionic separation. We observe a geometry-induced electro-diffusive coupling that inhibits solute dispersion in certain channels, leading to a non-monotonic Number of Theoretical Plates (NTP) and making such channels ideal for separation processes.

cond-mat.soft↗

arXiv2026

Electroosmotic lubrication flow in constricted microchannels with a compliant wall and DLVO interactions

We develop a nonlinear model for electroosmotic transport in a constricted microchannel with a compliant lower wall, with applications to soft microfluidics, bio-inspired sensing, and energy harvesting. The formulation couples electroosmotic slip-driven flow under a globally constrained electric field with pressure-driven lubrication and elastic wall deformation, modeled as a clamped Kirchhoff-Love plate. Short-range intermolecular stresses are incorporated through an extended Derjaguin-Landau-Verwey-Overbeek framework combining electrostatic double-layer repulsion and van der Waals attraction, enabling us to probe the nonlinear coupling between intermolecular forces, wall deformation, and electroosmotic flow in compliant microchannels. The flow is governed by six nondimensional parameters: wall compliance, geometric curvature, electrostatic and van der Waals strengths, scaled Debye length, and Dukhin number. Asymptotic analysis clarifies the role of these parameters in limiting regimes. In the stiff-wall limit, electroosmotic slip acts as a uniform offset to the pressure-driven flow. Fully coupled spectral collocation simulations confirm the asymptotic predictions and capture nonlinear feedback between pressure, deformation, and intermolecular stresses. Three regimes emerge: a stiff-wall regime with negligible deformation, a deformation-limited regime in which elastic narrowing strongly suppresses flux, and a repulsion-limited regime where DLVO forces cap wall deflection and prevent collapse. These results show how elasticity, geometry, and molecular forces jointly regulate electroosmotic lubrication and provide scaling rules for the design of compliant electrokinetic channels operating under nanometric confinement.

physics.flu-dyn↗

arXiv2020

Machine Learning in Materials Modeling -- Fundamentals and the Opportunities in 2D Materials

The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials have been successfully developed to predict material properties for various applications such as materials for photovoltaic cells, thermoelectric materials, dielectrics, materials for batteries, fuel cells, etc. The setup of comprehensive materials databases, and openly accessible algorithm frameworks have also spurred the usage of machine learning for solving some of the most pressing problems in materials science. Some such recent implementations are discussed in this book chapter. A multitude of two-dimensional (2D) materials exist with the potential to replace the conventional materials for energy storage and nanodevices. The challenges faced in designing batteries and how machine learning tools can help in screening and narrowing down on the best composition, as well as the synthesis of air-stable 2D materials, are also discussed.

cond-mat.mtrl-sci↗