Research

Catalytic transfer hydrogenation (CTH) is a  concept by whereby hydrogen donors are employed, instead of molecular hydrogen, to effect a hydrogenation or hydrogenolysis step. CTH, therefore, occurs at milder conditions and is particularly suited for distributed processing, such as in biomass conversion, the production of specialty chemicals, or in the treatment of wastes. CTH on metal catalysts can lead to synergies; for instance, in many cases, the activity and selectivity of CTH with a donor such as formic acid are higher than with molecular hydrogen. The mechanism of these reactions, however, is not completely well known. Using DFT and microkinetic modeling, we develop microkinetic models of representative CTH chemistries to explicate the role of hydrogen bonding and hydrogen atom transfer in enhancing the rate of hydrogen transfer compared to using molecular hydrogen. The results of this work would allow an understanding of how a donor can act as an additional dial to modulate activity and selectivity. 

Microkinetic modeling is a computational technique to develop a quantitative understanding of the mechanism catalytic reaction networks. It uses ab initio information (i.e. energetics of intermediates and transition states) to calculate information such as reaction rates, most abundant surface intermediates, dominant reaction pathways, rate-determining steps, etc.  In this research, the group is employing multifidelity machine learned models (i.e. models trained on few high fidelity data and relatively large low fidelity data), Bayesian inference, and nonlinear optimization to improve the predictive power of microkinetic models. Such improved models allow for a more complete understanding of the active sites and mechanism of complex catalytic chemistries. 

Liquid organic hydrogen carriers (LOHCs) are molecules that can carry hydrogen in the form of reversible chemical bonds and thereby offer a means of storage and transportation of new energy sources such as hydrogen from shale gas or electrons from renewable energy. In this research, the group is employing a spectrum of tools ranging from cheminformatics, density functional theory, graph theory, optimization, machine learning, and process design to develop a multiscale approach to systematically design new and improved LOHCs. 

Natural gas reserves often comprise of varying amounts of acidic gases such as CO2 and H2S. In this research, collaboration with Jonas Baltrusaitis, we are developing sulfur-tolerant catalysts (esp. transition metal sulfides) that simultaneously catalyze alkane activation, reverse water gas shift, and H2S dehydrogenation. In this context, we apply reaction kinetics studies, ex-situ and in-situ characterization, density functional theory (DFT), and microkinetic modeling to develop a fundamental understanding of the active site and mechanism of these reactions and design new transition metal sulfide catalysts.  

This NSF GOALI project is a partnership between Scientific Design Company, Inc. and Lehigh University (Prof. Israel Wachs and Srinivas Rangarajan) to develop a mechanistic understanding of the role of promoters on industrial catalysts for ethylene oxide synthesis. The project combines operando spectroscopy, steady-state kinetics, density functional theory, and microkinetic modeling to develop a mechanistic understanding of real catalysts. 

In collaboration with Jeetain Mittal and Mark Snyder, we are working on developing a framework to computationally design synthetically feasible covalent organic frameworks using cheminformatics, molecule generation, and Monte Carlo simulations of assembly and ultimately synthesize new materials for gas storage and molecule separation.