(364t) Computation-Led Design of Bimetallic Catalysts for Renewable Fuel Production

My research interests are solidly shaped by coupling my experiences growing up in crude oil-producing Nigeria with the research gaps that my knowledge has now elucidated in the field of computational heterogeneous catalyst design for renewable fuel production. I am deeply in favor of all the concerted efforts, especially in recent times, put in place to seek realistic alternatives to fossil fuel oil. The exploration and usage of fossil fuel oil leaves such devastating and lasting effects that require extended periods and significant financial outlay to clean up. Renewable oil has the most potential because it is the best substitute for fossil fuel oil in our internal combustion engines and is a more carbon-neutral alternative. However, generating a gallon of usable biofuel can cost up to five times as much as producing an equivalent amount of fossil fuel oil. The majority of this expense is related to the upgrading of bio-oil by lowering the high oxygen content (~40 wt%) to acceptable levels for carbon-based fuels (2 wt%). The significant oxygen content results in low heating value, low miscibility with other fuels, low energy density, increased polarity, instability, and corrosiveness. Thus, the overarching goal of my research is to computationally design catalysts that decrease biofuel production costs.

The most promising route for sustainable, renewable fuel production is via upgrading biomass-derived chemicals with bimetallic catalysts. However, such bimetallic catalysts often reconstruct and lose performance upon exposure to reaction environments. The gaps in designing bimetallic catalysts include inadequate connections between reaction conditions, surface reconstruction, and chemical insights into the synergy between constituent metals. Hence, there is a critical need to zoom down to nanoscale levels to characterize the interactions between relevant adsorbates and the metal constituents and connect them to the dominant surface features formed under different reaction environments. All of these nanoscale investigations then need to be scaled to macroscale levels to obtain usable metrics to adequately understand, gauge, predict, and tune catalytic reconstructions and performance. Employing solid theoretical concepts to translate fundamental nanoscale insights into macroscale, real-world results have potential applications to the general design of heterogeneous bimetallic catalysts for wide ranges of important chemical reactions. These approaches will advance the production of biofuels toward viable commercial levels.

Overall, whether it is focusing on computational research or foraying into experimental designs to investigate chemical processes, the common denominator in my research interest is the creation of models that mimic reality as much as possible. Such models smartly incorporate components that address concerns that often lead to discrepancies in the results from the models and reality, all in a quest to sustainably produce fuels and valuable chemicals.

My skills based on accomplished projects include:

• Density Functional Theory (DFT) (ground states, transition states).
• Ab initio Molecular Dynamics (AIMD).
• Nanoparticle (NP) Modeling (Original code).
• Experimental Collaboration.
• Microkinetic Modeling.
• High-Performance Computing Experience.
• Construction of Machine-Learning Interatomic Potentials (ML-IAPs).
• Coverage and Configuration Mapping.
• Classical Molecular Dynamics.
• Machine- and Deep-Learning using Machine-Learning Algorithms, Artificial Neural Network (ANN), and Convoluted Neural Network (CNN).
• Data Analysis and Visualization.
• Time and Project Management.
• Writing Proposals, Scientific Papers, and Reports and Presenting Technical and Scientific Discoveries.
• Integration of Multiple Disciplines.
• Critical Problem-Solving Skills.
• Updated Knowledge of Relevant Technologies.
• Leadership and Mentorship.

Awards

• Frontera Computational Science Fellowship Award, $34,000 in stipend, $12,000 in tuition allowance, and 50,000 CPU hours awarded, 2024-2025.
• Catalysis and Reaction Engineering (CRE) Division Student Travel Award for the 2023 Annual AIChE Meeting, $400 awarded, 2023.
• Stevens Graduate Conference Funding for the 2023 Annual AIChE Meeting, $1,500 awarded, 2023.
• Kokes’ Award for the 28th North American Meeting (NAM) of the Catalysis Society, ~$1,700 in hotel fees awarded, 2023.
• Provost Doctoral Fellowship Award from Stevens Institute of Technology, $63,206 yearly awarded, 2021.

Selected Publications

• Omoniyi, A.; Hensley, A.J.R., Coverage and Facet Dependent Multiscale Modeling of O* and H* Adsorption on Pt Catalytic Nanoparticles, Phys. Chem. C., 2024, 128, 7073-7086.
• Pipitone, G.; Hensley, A.J.R.; Omoniyi, A.; Zoppi, G.; Pirone, R.; Bensaid, S., Unravelling Competitive Adsorption Phenomena in the Catalytic Valorization of Wastewater Streams: An Experimental and Theoretical Study, Chem. Eng., 2024, 482, 148902.
• Furrick, I.; Omoniyi, A.; Wang, S.; Robinson, T.; Hensley, A.J.R., Integration of Facet-Dependent, Adsorbate-Driven Surface Reconstruction into Multiscale Models for the Design of Ni-Based Bimetallic Catalysts for Hydrogen Oxidation, ChemCatChem, under review 2024.
• Garzon, A.; Wang, S.; Omoniyi, A.; Tam, L.; Che, F.; Hensley, A.J.R., Temperature and Pressure Driven Functionalization of Graphene with Hydrogen and Oxygen via Ab Initio Phase Diagrams, Surf. Sci., under review 2024.
• Omoniyi, A.; Bensaid, S.; Pipitone, G.; Hensley, A.J.R., Impact of Intermolecular Interactions in the Adsorption of Carboxylic Acids on Pt(111), in-preparation, 2024.
• Omoniyi A.; Vadehra M.; Thompson C.; Braslavets S.; Duggan S.; Hensley A.J.R., A Comprehensive Analysis of the Process, Economics, and Business Model of Converting Used Cooking Oil to Biodiesel in the Northeastern United States, in-preparation, 2024.

Scholarly Achievements

• Research Grants: 2 fellowships and 3 conference grants.
• Conference Oral Presentations: 3
• Conference Poster Presentations: 3