RII Track-2 FEC:Cost-effective Conversion of Natural Gas and Biomass to Hydrogen and Performance Carbons

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In this multidisciplinary and bi-institutional collaborative project, researchers from the University of Oklahoma and Iowa State University will investigate fundamental principles that will accelerate the development of sustainable and cost-effective processes for production of carbon-free or carbon-negative hydrogen and value-added advanced materials, including carbon nanotubes and carbon nanofibers. In the past, processes to obtain these advanced materials have been developed based on niche high-value solid products, without focusing on the value or utilization of the gaseous by-product at a large scale. Here, the emphasis is in the large-scale and low-cost production of CO2-free hydrogen. Namely, the project will focus on enhancing hydrogen yields from abundant natural gas resources using earth-abundant metals as catalytic materials, which need to be reclaimed from the solid product for environmental and technoeconomic reasons. Therefore, a central part of the project is the optimization of hydrogen yield and catalyst recyclability. A secondary but equally important part from a technoeconomic standpoint is the property optimization of the resulting performance carbon materials that could be employed in large-scale applications. In the latter half of the project, other emerging high-impact technologies, such as carbon-free ammonia production and environmentally benign energy from waste plastics will also be explored.This project includes researchers with complementary expertise ranging from experimental and theoretical modeling and technoeconomic modeling of the catalytic processes to materials synthesis and characterization of reactive catalytic surfaces as well as characterization of the produced carbon nanomaterials. The research plan contemplates experimental kinetics based on realistic models with specified sequences of elementary steps, characterization of the materials and processes at the nanoscale, and computational analysis ranging from first-principles density functional theory (DFT) and ab-initio molecular dynamics (AIMD) calculations. Machine learning and artificial intelligence methods will be incorporated in the project to connect the various modeling scales and predict promising surface structures. This project will also carry a significant community outreach component, including research opportunities for underrepresented undergraduate students, as well as workshops with hands-on activities aimed at high school students to better educate students on the importance of technology in energy generation while avoiding or minimizing greenhouse gas emissions. The team will pair with regional MSI’s and tribal colleges to create research opportunities for underrepresented groups, such as Native American students, to encourage and empower them to pursue graduate degrees in engineering.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.