Research Projects

Catalysts play a vital role in a wide array of chemical processes by lowering activation energies, enhancing reaction rates, and enabling the sustainable and economically viable production of chemicals. So, the development of high-performance catalysts has remained a key focus of research across both academia and industry. In the past decade, single-metal-atom catalysts (SMACs) supported on nitrogen-doped graphene have emerged as promising candidates in catalysis due to their high atomic efficiency and well-defined coordination environment. However, they are inherently limited by their mononuclear active sites, which reduce their ability to efficiently facilitate competitive adsorption of different reactants, such as in hydrogenation processes where both H2 and a substrate must bind to the catalyst surface. More recently, dual-metal-atom catalysts (DMACs) supported on nitrogen-doped graphene have evolved from SMACs, offering better substrate interactions and greater structural flexibility at the two metal sites. Compared to SMACs, DMACs enable enhanced performance through synergistic effects between metal atoms. This versatility allows DMACs to catalyse a wider range of reactions. Recently, various DMACs have been reported, demonstrating notable selectivity and activity over SMACs for numerous hydrogenation reactions. Despite this progress, existing literature on graphene-supported DMACs in hydrogenation processes lacks definitive insights into how different transition metals, and their oxidation and spin states, influence the catalytic activity and selectivity. In our preliminary investigations using a Density Functional Theory (DFT)-based small computational model of a DMAC, we noticed that the substrate binds to a single metal atom, while H2 activation requires the assistance of two metals, thus showing the importance of dual-metal sites. So, there is a need to systematically explore how various metal pairs activate H2 and transfer hydrogens to the substrate. We further observed that both stability and reactivity are highly sensitive to the spin state of the metal centers. In our investigations, we found significant spin contamination in some of the DFT computations, indicating the necessity for a multireference computational approach. Specifically, a Complete Active Space Self-Consistent Field (CASSCF) study, followed by N-electron valence state second-order perturbation theory (NEVPT2) calculations, is warranted to obtain a more accurate understanding of the electronic structure and reaction pathways. Moreover, our computational catalysis group aims to provide orbital-level mechanistic insights into hydrogenation processes with a particular focus on unravelling the roles of metal-metal and metal-support cooperativities in governing catalytic activity and stability. Supported by our fundamental computational studies, the broad objective is to establish a systematic protocol for the rational design of efficient DMACs for the hydrogenation of nitroarenes, acetylene, styrene, and carbon dioxide. Traditional strategies for designing DMACs still rely on trial-and-error experimentation, which is further hindered by synthetic limitations and challenges in in situ characterization. Recent advances in quantum calculations and computational infrastructure provide a promising alternative for rational catalyst design through theoretical modelling. However, computing all possible catalysts using quantum-based methods is computationally intensive. So, machine learning will also be used to accelerate this process by uncovering key trends and structure-activity descriptors. This proposal aims to develop fundamental electronic structure understanding of DMACs-assisted hydrogenation processes. The insights from our studies will advance the field of DMACs, which will be utilized by chemists, material scientists and chemical engineers. Our exploration of catalysts design will also support future industry processes.