In the times of an ever-increasing rate of global warming and rapidly depleting fossil fuels, renewable sources of energy are attracting vast attention in the scientific community. Over the past couple of decades, numerous efforts have been directed towards switching over to a hydrogen-based economy with the aim of bringing down combustion-associated emissions. However, so far, the biggest technical challenge in doing so has been the development of materials and the required infrastructure for efficient storage and transportation of hydrogen. To this end, the use of Liquid Organic Hydrogen Carriers (LOHCs) has been the focus of a number of studies over the past couple of decades. Since the release of hydrogen from LOHCs requires the use of catalysts, a 'hydrogen economy' is only feasible when expensive, noble metal catalysts that are traditionally used in dehydrogenation reactions are substituted with inexpensive but equally efficient alternatives.<br/><br/>In this search for alternate catalysts, we employ our group’s cyberinfrastructure for the data-driven discovery and design of new chemistry that entails virtual molecular library generation and high-throughput screening of large-scale candidate libraries. We have developed a computational protocol for the dehydrogenation reaction of LOHCs which encompasses identification of descriptors for the catalytic activities associated with these pathways. For the dehydrogenation of perhydro-N-ethyl carbazole as the model LOHC candidate, thermodynamic and kinetic parameters such as activation barriers and the rate determining step are calculated for various classes of pincer catalysts using Density Functional Theory. The results thus obtained are then benchmarked against experimental values. This protocol is then cast to molecular screening libraries generated from our group’s library generator package, ChemLG. The libraries created from ChemLG span several metal centers as well as side-chain substitutions on a base pincer catalyst. The massive data thus collected is then cast through our machine learning and informatics program package for the analysis of hidden structure-property relationships in these complex systems.

Evaluating homogeneous catalysts for hydrogen extraction from liquid organic hydrogen carriers

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.