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AI-Augmented Physics-Aware Energy Management for Degradation-Conscious Industrial Battery Storage

Problem Statement: Industrial BESS energy management systems typically rely on simplified aging assumptions, treating battery degradation as proportional to energy throughput and neglecting operating-condition dependence. Such simplifications lead to aggressive charging and discharging strategies that reduce battery lifetime and distort the true economic value of storage, particularly under variable tariffs, temperatures, and industrial load profiles.

Machine learning model for enzymatic cascade

Enzymatic cascades are conserved set of biochemical reactions, ubiquitously found in many systems. The goal of this project is to develop a suitable machine learning model to predict the reaction structure and kinetics of an enzymatic cascade. The project will involve use of systematic kinetic model simulations of a repertoire of cascades in the context of machine learning models. Candidate is expected to have a strong interest in reaction engineering and have basic training in python programming.

Lattice Boltzmann Modeling of Rarefied Flows in Complex Porous Geometries

The goal is to develop a general lattice Boltzmann model for high (~10^{-3}) Knudsen numbers flow in porous media. The challenges involve maintaining numerical stability at high Knudsen numbers in complex geometries. Focus will be on specular boundary conditions. The project will involve building and testing a mathematical model with C++/Python code. 

Nanoporous gold particles: Modeling selective dissolution of active metal species from gold alloy

Dealloyed gold nanoparticles can be synthesized by selectively dissolving Ag from gold-silver alloy nanoparticles through the well-known process of dealloying. These nanoparticles exhibit remarkable catalytic activity towards the CO oxidation reaction owing to their large specific surface area, presence of rough surfaces that contain a high density of catalytically-active sites, and synergistic effects arising from the residual Ag leftover from the dealloying process.

Microkinetic Modelling of Dry Reforming of Methane over Supported Ni

Microkinetic modeling is powerful tool in heterogeneous catalysis, as it quantitatively merges fundamental surface chemistry principles with experimental data, avoiding the need for prior assumptions about rate-determining steps (RDS), quasi-equilibrated steps, or most abundant reaction intermediates (MARI). This project focuses on modeling of green, heterogeneously catalyzed reactions that generate H2 and syngas (H2 + CO), such as dry reforming of methane (DRM).

Catalyst and reactor development for sustainable CO2 Utilization and Storage

Utilization of CO2 as a renewable feedstock to produce fuels/chemicals is a potential way to mitigate the effects of anthropogenic climate change. However, CO2 conversion technologies are still at a nascent stage and are limited by several technical challenges. Development of active, selective, and stable heterogeneous catalysts is key to the development of such technologies. This project will focus on the synthesis of tailor-made catalysts for the catalytic conversion of carbon dioxide into value-added chemicals or syngas or solid carbon with/without light irradiation.

Catalyst and reactor development for sustainable CO2 Utilization and Storage

Utilization of CO2 as a renewable feedstock to produce fuels/chemicals is a potential way to mitigate the effects of anthropogenic climate change. However, CO2 conversion technologies are still at a nascent stage and are limited by several technical challenges. Development of active, selective, and stable heterogeneous catalysts is key to the development of such technologies. This project will focus on the synthesis of tailor-made catalysts for the catalytic conversion of carbon dioxide into value-added chemicals or syngas or solid carbon with/without light irradiation.