(4lk) Mechanistic Insights and Catalyst Design for Biomass Conversion: A Multiscale Approach

Research Interests: I aim to advance the understanding and design of catalysts for sustainable chemical transformations, particularly in bio-renewable energy processes. By combining quantum mechanical calculations (DFT), ab initio molecular dynamics (AIMD), and microkinetic modelling (MKM), I plan to unravel complex reaction mechanisms and optimize catalyst performance. Additionally, I will leverage machine learning to accelerate catalyst screening, enabling the discovery of more efficient and cost-effective materials. My goal is to contribute to the development of innovative catalytic systems that address pressing environmental and energy challenges.

Abstract

Heterogeneous catalysis is pivotal in industrial processes, driving over 20% of global chemical conversions. Yet, a comprehensive understanding of surface phenomena and atomic-level reactions remains a significant challenge. The application of Density Functional Theory (DFT) calculations, particularly in determining reaction and activation energies, provides valuable insights into catalyst activity and stability. This is especially crucial for complex biomass chemistry reactions, such as the reductive amination of 5-HMF. Through DFT and ab initio microkinetic modelling (MKM), we explored the detailed mechanistic pathways on metal catalysts, identifying turnover frequencies, selectivity, and the rate-determining step. Our findings led to the proposal of a bimetallic catalyst capable of mitigating deactivation due to strong nitrogen adsorption.

Furthermore, the morphology of nanoparticles significantly influences their exposed surface facets, leading to variations in catalytic activity. Solvents also play a critical role in dictating reactivity and selectivity. To elucidate these effects, we investigated structure-dependent activity and selectivity in the furfural acetalization reaction, using alcohol solvents, over well-defined supported Pd nanostructures. Combining experimental data with DFT simulations, including explicit solvent models, we demonstrated that hydrogen bonding between solvent molecules and adsorbates facilitates proton transfer, reducing activation barriers and stabilizing transition-state structures.

We also examined the role of the metal/solvent interface in the hydrogenation of biomass-derived platform molecules. Using both implicit solvent models (VASPsol) and explicit solvent environments in ab initio molecular dynamics (AIMD) simulations, we unravelled the hydrogenation mechanism under these conditions.

Given the challenges of screening active and selective catalysts from DFT simulations, due to the vast array of catalytic materials and the high computational costs, we leveraged DFT-derived energetics and elemental periodic properties to train machine learning models. These models accurately predicted binding energies for key reaction intermediates, such as CO and OH, on Cu-based bimetallic systems. The predicted binding energies were subsequently integrated into ab initio MKM to forecast turnover rates and selectivity for the reverse water-gas shift reaction, facilitating the identification of cost-effective Cu-based bimetallic catalysts.

Collectively, these studies offer a robust framework for the rational design of metal and bimetallic catalysts, providing an effective strategy for catalytic transformations of bio-renewable substrates.

Teaching Interests

I am well-qualified to teach core undergraduate chemical engineering courses, with specific interests in Heat Transfer, Fluid Mechanics, Chemical Reaction Engineering, and Thermodynamics. During my PhD and master's, I gained extensive teaching experience as a teaching assistant, handling Applied Chemistry, Process Control, and Chemical Reaction Engineering laboratories. I also taught master’s-level courses, including Molecular Modelling of Catalytic Reactions and Heterogeneous Catalytic Reaction Engineering, where I designed and delivered tutorials and lectures.

For the past three years, I have been actively involved in undergraduate teaching as part of my responsibilities as a Prime Minister’s Research Fellow (PMRF). My teaching philosophy centres around active learning, where I encourage students to present research papers related to course topics, engage in critical discussions, and apply their knowledge through mini-projects using computational tools. By integrating problem-based learning and fostering an interactive classroom environment, I aim to enhance student engagement and deepen their understanding of fundamental and advanced chemical engineering concepts.

Additionally, I emphasize interdisciplinary thinking and the practical application of theoretical knowledge. My approach helps students bridge the gap between theory and industry-relevant skills, equipping them with tools for problem-solving in real-world scenarios. I am committed to nurturing a collaborative learning atmosphere and mentoring students through both coursework and research projects, aligning with the institution’s educational goals.

Bio sketch

I am a Ph.D. candidate in the Department of Chemical Engineering at the Indian Institute of Technology Hyderabad and a recipient of the prestigious Prime Minister’s Research Fellowship (PMRF) awarded by the Government of India. With over four years of experience in molecular simulations including Density Functional Theory (DFT), ab initio microkinetic modelling (MKM), and ab initio molecular dynamics (AIMD), my research focuses on providing atomic-level insights into catalytic processes. I have also integrated machine learning techniques to accelerate catalyst screening and optimize reactions in biomass chemistry. My work aims to bridge computational models with experimental reality, guiding the design of more efficient and sustainable catalysts. I am seeking full-time opportunities in academia or industry.