Jithin John Varghese

Two-dimensional metal organic frameworks (2D MOFs) are a class of next generation materials for adsorbents,membranes and sensors; however, many 2D MOF synthesis methods lack the scalability and precision required for translation to industrial scales. Furthermore, the engineering of 2D MOF structures is challenged by complex environment-surface interactions that arise from their high anisotropy, thinness and functionally diverse surfaces. In this work we developed new understandings and methods of engineering such structures by using accelerated, high shear synthesis, and solvent exchange. With the recently developed annular flow microreactor we synthesized 2DMOFs more efficiently than conventional batch methods, by up to 5 orders of magnitude in terms of reactor space-time-yield. To accurately characterize particle size and dynamics in various organic solvents, we used liquid cell transmission electron microscopy. This technique not only visualized the oriented attachment of nanosheets, but also showed that the rate and direction of attachment is significantly influenced by solvent-surface interactions. These techniques and understandings provide rational bases for 2D MOF engineering and process design.

Using periodic Density Functional Theory calculations, propane oxidative dehydrogenation (ODH) and overoxidation over bare and hydroxylated Ni-doped CeO2 nanorods with predominantly exposed (1 1 0) facets were studied. Ab-initio thermodynamics-based surface phase analysis and computational Raman spectroscopic analysis predicted an 8.3 % surface oxygen vacancy concentration (Ce0.83Ni0.17O1.83) at typical ODH conditions. Only one-third of the surface oxygens, adjacent to the dopant, were selective for propene formation. Moreover, activated oxygen (O22–*) favored the formation of overoxidation products over propene. A monolayer hydroxyl coverage from water dissociation was stable at typical ODH conditions. This reduced the activation energy barrier for propene formation by 0.38 eV, increased the barrier for undesired acetone formation by 0.54 eV, and increased the barrier for propene activation by 0.6 eV. These promotional effects were due to the destabilization and induced hyperconjugation effects in the C3 adsorbates due to surface hydroxylation. Hence, surface hydroxylation (Lewis Base addition) is a potential strategy to improve propene selectivity.

The liquid-phase direct partial oxidation of methane to methanol is highly desirable for methane utilization. Using a combination of density functional theory and molecular dynamics simulations, this investigation reports the impact of sulfolane solvent on the reaction mechanism of methane oxidation with H2O2 in Fe-ZSM-5 catalyst and the potential product distribution. Sulfolane favored the formation of [Fe-OH] and [Fe =O] as active oxygen species from H2O2 dissociation, unlike Fe-OOH in water. On [Fe-OH], methanol formation was via a methoxy intermediate (activation barrier, Ea = 1.79 eV), while on [Fe O], it was by the radical rebound mechanism (Ea = 1.41 eV). The formation of formaldehyde was kinetically and thermodynamically less favorable than methanol and the formation of formic acid had a higher barrier. The lower diffusivity of H2O2 than CH3OH and the lower diffusivity of these in sulfolane than in water, together with the trends in reaction barriers, indicate higher methanol selectivity in sulfolane than in water.

Catalytic upgrading of biowaste to chemicals and fuels is a key step towards circular economy. Computational investigations of catalytic upgrading of biowaste have made valuable contributions towards catalysts design. Recent applications of 1) Density Functional Theory (DFT) and molecular dynamics (MD) simulations, and microkinetic modelling to derive insights on active sites, reactions mechanisms, solvation and catalyst deactivation, 2) DFT and MD simulations, multiscale models, and statistical methods for identification of structure of catalyst nanoparticles, 3) DFT calculations and statistical methods for identification of catalytic descriptors and development of scaling relations, are discussed. A recent trend is to exploit advances in data science and data repositories for catalysis. There is immense potential for combined multiscale computational techniques and machine learning to enable in silico design of catalysts for the upgradation of biowaste.

Solvents are crucial components in specialty chemical and pharmaceutical industries and in electrochemical and photoelectrochemical processes, and are increasingly being used in catalytic reactions. Solvents significantly influence the kinetics and thermodynamics of reactions and can alter product selectivity markedly. While such solvent effects are observed routinely, identification of the root causes of such effects is less frequent. Solvents can influence reaction rates, conversion and product selectivity by 1) directly participating in the reaction steps and opening alternate reaction pathways, 2) competing with the reactant for interaction with the catalysts, 3) changing the relative stabilization of the reactant, the transition state (TS) and/or the product, 4) altering intra-pore diffusion characteristics in porous catalysts, 5) exhibiting entropic confinement effects altering free energy barriers of reactions, 6) changing the solubility of different components in the reaction mixture, and 7) inhibiting undesired reactions. Their indirect influences may be due to 1) changes brought on to active sites on catalysts and 2) altered structure/stability of catalysts. This article discusses these fundamental reasons behind observed solvent effects with suitable examples. Advances in computational chemistry have led to the development of multiple tools and techniques, considering solvents either as implicit or as explicit molecules, providing molecular insights into complex solvent effects in catalysis. This article provides an overview of some of these methods with suitable examples to demonstrate their application and potential. This mapping of the solvent effects and their origins is believed to aid in the rational selection of solvents for catalytic reactions. The description of the computational tools, their application and their potential is likely to encourage widespread use of these techniques to investigate solvent effects.

DFT simulations predicted the C-O bond cleavage in glycerol on Brønsted acidic Pt-WOx catalyst under a hydrogen atmosphere to be via a “protonation dehydration” mechanism. Interfacial Pt-WOx sites facilitate Brønsted acid (BA) site formation by hydrogen spillover and synergistic activity of Pt and WOx. At strong BA sites, selective formation of 1,3-PDO is likely due to the large difference of over 40 kJ/mol in primary and secondary C-O bond cleavage barriers. The secondary C-O bond cleavage has a linear relation with NH3 binding energy while there is a non-monotonic trend for the primary C-O cleavage. Hence, Brønsted acid strength is a potential descriptor for the catalyst activity and 1,3-PDO selectivity. Synthetic strategies enabling fine dispersion of WOx to trimeric units on the Pt nanoparticles, while preventing W doping and agglomeration of WOx to 3D WO3 on small Pt nanoparticles are desirable for high BA strength and thereby 1,3-PDO yield.

Due to their high anisotropy and tunable chemical composition, two-dimensional metal organic frameworks (2D MOFs) have great potential as building blocks for next-generation materials in a diverse range of applications - from electrochemical catalysis to membrane separation. However, the controllable synthesis is complicated by the environment-surface interactions that arise from the high anisotropy, thinness, and functionally diverse surfaces of 2D MOFs. Liquid cell transmission electron microscopy (LCTEM) offers a unique opportunity to study these interactions in situ. In this work, we analyzed the effects of different solvent environments on the structure and aggregation dynamics of copper benzene dicarboxylic acid (CuBDC) nanosheets, which were synthesized using a high shear annular microreactor. LCTEM revealed that 2D MOF nanosheets undergo oriented attachment and that the rate and direction of oriented attachment is controlled by solvent-surface interactions. We investigated the nature of these solvent interactions using density functional theory calculations, which suggest that the binding energy of solvents to different MOF surfaces is likely responsible for this behavior. The CuBDC nanosheets were then applied as adsorbents in organic solvents, in which we showed how solvent-mediated oriented attachment could significantly affect adsorption properties.

The aim of this work is to develop insights into adsorption of hydrogen and carbon dioxide in a zeolitic imidazolate framework, ZIF-8, using high-pressure adsorption studies, adsorption isotherm model fitting, and DFT investigation of preferential adsorption sites and binding energies. The robustness of ZIF series metal–organic frameworks has drawn interest towards its utility in large scale applications in gas storage and separation. We use room temperature synthesis of ZIF-8 using DMF as a solvent, and benchmarked it against typical solvothermal synthesis. The resulting material is characterized using XRD, SEM, TG–DSC and N2 adsorption isotherm. High-pressure volumetric adsorption of the activated materials is conducted to analyze the hydrogen and carbon dioxide storage capacities up to 50 and 40 bar, respectively. ZIF-8 shows maximum H2 storage capacity of 3.13 wt% at 50 bar and 77 K, and CO2 storage capacity of 46 wt% at 40 bar and 300 K. The parameters of Unilan adsorption isotherm are estimated from the equilibrium adsorption data and isosteric heats of adsorption for H2 and CO2 on ZIF-8 are computed. DFT calculations are used to obtain preferential adsorption sites of H2 and CO2. Adsorption enthalpy values were calculated from DFT as − 7.08 and − 25.98 kJ/mol, respectively for H2 and CO2 at the most preferred sites. We found a close agreement between isosteric heat of adsorption of hydrogen (− 4.68 kJ/mol) and the enthalpy of hydrogen adsorption from DFT (− 6.04 kJ/mol) at 77 K.

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Cu/ZnO/ZrO2/Al2O3 catalysts are widely explored for CO2 conversion to methanol due to their higher activity and stability. However, mechanistic understanding of the performance of such catalysts is lacking due to ambiguity on the actual active sites. This study focuses on unraveling the nature of different interfaces on Cu/ZnO/ZrO2/Al2O3 catalyst by coupling experiments, Density Functional Theory (DFT) simulations and a DFT-based reactor scale multi-site microkinetic model. Although DFT calculations suggested the ZrO2/Cu interface to be the CO2 adsorption site, the validated microkinetic model predicted the ZnO/Cu interface to be the crucial reaction center. Reaction pathway analysis showed that methanol is produced through the formate pathway near the reactor entrance, whereas, the carboxyl pathway dominates in the latter zones, emphasizing the occurrence of both CO2 and CO hydrogenation. This deeper understanding of the reaction behavior of such multicomponent catalysts will aid in designing better catalysts and optimizing reaction conditions and systems.