arXiv subjects

Neda Nazemifard

Publications and source records attributed to Neda Nazemifard.

5 recordsLinked to original sources

arXiv2026

Integrated Population Balance and Multiphysics Modeling for Predicting Undesired Agglomeration in Small Molecule Manufacturing

Agitated filter dryers (AFDs) are a crucial unit operation in small molecule manufacturing that enables simultaneous filtration, washing, and drying of active pharmaceutical ingredients. One of the key challenges in AFDs is associated with undesired agglomeration, where the presence of hard agglomerates results in off-spec products, equipment damage, and additional downstream processing. This article presents a novel mechanistic model that describes the formation of soft and hard agglomerates during agitated filter drying. By integrating population balance and multiphysics modeling, the model can accurately predict the evolution of the product temperature, moisture content, and particle size distribution, and hence quantify the extend and impact of undesired agglomeration across various operating conditions. Our proposed model-based framework enables the rational design and operation of AFDs for improving the product quality and process reliability.

cs.CE↗

arXiv2025

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equations (Navier-Stokes for fluid flow, material balance for reactive species transport, and energy balance for temperature distribution) into a unified multi-scale network model. The framework is composed of three interconnected sub-networks with independent optimizers that enable both forward and inverse problem-solving. In the forward mode, FMEnets predicts velocity, pressure, species concentrations, and temperature profiles using only inlet and outlet information. In the inverse mode, FMEnets utilizes sparse multi-residence-time measurements to simultaneously infer unknown kinetic parameters and states. FMEnets can be implemented either as FME-PINNs, which employ conventional multilayer perceptrons, or as FME-KANs, based on Kolmogorov-Arnold Networks. Comprehensive ablation studies highlight the critical role of the FMEnets architecture in achieving accurate predictions. Specifically, FME-KANs are more robust to noise than FME-PINNs, although both representations are comparable in accuracy and speed in noise-free conditions. The proposed framework is applied to three different sets of reaction scenarios and is compared with finite element simulations. FMEnets effectively captures the complex interactions, achieving relative errors less than 2.5% for the unknown kinetic parameters. The new network framework not only provides a computationally efficient alternative for reactor design and optimization, but also opens new avenues for integrating empirical correlations, limited and noisy experimental data, and fundamental physical equations to guide reactor design.

cs.LG↗

arXiv2017

Thermal graphene metamaterials and epsilon-near-zero high temperature plasmonics

The key feature of a thermophotovoltaic (TPV) emitter is the enhancement of thermal emission corresponding to energies just above the bandgap of the absorbing photovoltaic cell and simultaneous suppression of thermal emission below the bandgap. We show here that a single layer plasmonic coating can perform this task with high efficiency. Our key design principle involves tuning the epsilon-near-zero frequency (plasma frequency) of the metal acting as a thermal emitter to the electronic bandgap of the semiconducting cell. This approach utilizes the change in reflectivity of a metal near its plasma frequency (epsilon-near-zero frequency) to lead to spectrally selective thermal emission and can be adapted to large area coatings using high temperature plasmonic materials. We provide a detailed analysis of the spectral and angular performance of high temperature plasmonic coatings as TPV emitters. We show the potential of such high temperature plasmonic thermal emitter coatings (p-TECs) for narrowband near-field thermal emission. We also show the enhancement of near-surface energy density in graphene-multilayer thermal metamaterials due to a topological transition at an effective epsilon-near-zero frequency. This opens up spectrally selective thermal emission from graphene multilayers in the infrared frequency regime. Our design paves the way for the development of single layer p-TECs and graphene multilayers for spectrally selective radiative heat transfer applications.

physics.optics↗

arXiv2016

Using Microfluidic Device to Study Rheological Properties of Heavy Oil

In this study, capillary-driven flow of different pure liquids and diluted bitumen samples were studied using microfluidic channel (width of 30 um and depth of 9 um). Capillary filling kinetics of liquids as a function of time were evaluated and compared with theoretical predictions. For pure liquids including water, toluene, hexane, and methanol experimental results agreed well with theoretical predictions. However, for bitumen samples, as concentration of bitumen increased the deviation between theoretical and experimental results became larger. The higher deviation for high concentrations (i.e. above 30%) can be due to the difference between dynamic contact angle and bulk contact angle. Microchannels are suitable experimental devices to study the flow of heavy oil and bitumen in porous structure such as those of reservoirs.

cond-mat.soft↗

arXiv2016

Axial Super-resolution Evanescent Wave Tomography

Optical tomographic reconstruction of a 3D nanoscale specimen is hindered by the axial diffraction limit, which is 2-3 times worse than the focal plane resolution. We propose and experimentally demonstrate an axial super-resolution evanescent wave tomography (AxSET) method that enables the use of regular evanescent wave microscopes like Total Internal Reflection Fluorescence Microscope (TIRF) beyond surface imaging, and achieve tomographic reconstruction with axial super-resolution. Our proposed method based on Fourier reconstruction achieves axial super-resolution by extracting information from multiple sets of three-dimensional fluorescence images when the sample is illuminated by an evanescent wave. We propose a procedure to extract super-resolution features from the incremental penetration of an evanescent wave and support our theory by 1D (along the optical axis) and 3D simulations. We validate our claims by experimentally demonstrating tomographic reconstruction of microtubules in HeLa cells with an axial resolution of $\sim$130 nm. Our method does not require any additional optical components or sample preparation. The proposed method can be combined with focal plane super-resolution techniques like STORM and can also be adapted for THz and microwave near-field tomography.

physics.optics↗