(5h) Upgradation of Biomass-Derived Platform Molecules to Fuels and Chemicals By Rationally Designing Transition-Metal Based Catalysts Using DFT Simulations

Over the past two decades, Density Functional Theory (DFT) simulations have played pivotal role in rational design of various heterogenous catalysts. This research is centered on systematic design of transition metal-based catalysts for transformation of biomass-derived platform molecules like 5-Hydroxymethylfurfural (5-HMF) and furfural into valuable fuels and chemicals.

We used DFT simulations to understand the reaction mechanism for reductive amination of 5-HMF to 2,5 bis (amino methyl)-furan (BAMF) on Ni (111) surface. Based on activation and reaction energies, a pathway was proposed which included experimentally evident intermediates like 5-hydroxymethyl furfuryl amine (HMFA), imine-compound, etc. Strong binding energy (BE) of nitrogen (-561 kJ/mol) on Ni (111) was found to deactivate the catalyst. Furthermore, ab initio microkinetic model (MKM) was developed to calculate turn-over frequencies and rate-determining step.

Choice of solvents and facet types also influence catalytic reactions. This was studied using furfural acetalization as a model reaction, on Pd nanostructures in different alcohols at 303 K and H₂ pressure. Furfural conversion followed: cubes (78%) > octahedra (18%) > spheres (6%) in ethanol solvent. Conversion on cubes increased to 92% in methanol solvent. The calculated activation barriers matched the experimental trends of higher reactivity of Pd cubes in methanol solvent. DFT calculations highlighted the role of hydrogen bonding in facilitating proton transfer, leading to stabilization of transition-state structures and reduction of barriers.

Finally, we used ML models to predict BEs of OH and CO descriptors on A₃B type bimetallic alloys using readily available periodic features. Extreme gradient boosting regression (x-GBR) gave superior performance with rmse of 0.12 eV and 0.23 eV for predicting CO and OH bindings. BEs predicted by our ML models can be further integrated with ab initio MKM with which catalytic rates for various reactions like methanol electro-oxidation, formic acid decomposition to H₂ etc., can be calculated.