A promising route to olefins that are important building blocks for the chemical industry is the dehydrogenation of alkanes on metal oxides. This reaction occurs due to oxide acid-base surface functionalities, but the underlying mechanism is poorly understood. This knowledge gap has been impeding efforts to design active and robust catalysts that efficiently convert natural gas (i.e. light hydrocarbons) to value-added chemicals (i.e. olefins), optimize natural gas upgrading, and reduce the cost of the energy-demanding dehydrogenation chemistries. The proposed research aims to fill this knowledge gap by elucidating structure-activity relationships on metal oxide catalysts applicable to the dehydrogenation of light alkanes, using first-principles-based computational techniques and experimental verification through collaboration with researchers from the RAPID Manufacturing Institute. The project has the potential to positively impact the chemical industry by accelerating catalyst discovery for the efficient conversion of light hydrocarbons from natural gas to value-added chemicals. The development of efficient processes that reduce the energy input and associated cost for chemicals production, and utilize the abundant natural gas reserves, can have a positive impact on society and the US economy.
The proposed research plan includes application of periodic Density Functional Theory calculations, ab-initio molecular dynamics simulations, multi-scale process modeling and machine learning to develop novel structure-activity relationships that describe the alkane dehydrogenation activity as a function of the Lewis acid-base properties of the oxides. These relationships will enable the rapid screening of a wide range of metal oxides, different facets on each oxide and a gamut of surface acid-base sites, to identify the prevailing dehydrogenation mechanism and the most active sites on the metal oxides, as function of their Lewis acid-base strength. The project will also create poisoning maps by identifying which surface sites will be poisoned by strong species adsorption, even at elevated dehydrogenation temperatures, and will study catalyst surface dynamics under realistic experimental conditions. The ultimate objective is to advance catalyst discovery by avoiding trial-and-error experimentation in the laboratory, address knowledge gaps pertinent to process intensification, and support translational research being conducted in the RAPID Manufacturing Institute. The educational and outreach components of the proposal include: (a) engaging graduate and undergraduate students in research, with a focus on students from under-represented backgrounds, (b) generating material for relevant courses (Chemical Kinetics, Catalysis, etc.) and (d) enabling STEM outreach to K-12 students through the Carnegie Science Center and University of Pittsburgh's INVESTING NOW program. Students will be trained on computational chemistry, molecular simulations, catalysis, process intensification, scientific computation, machine learning and high-performance computing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
