The initial steps of biomass refining involve breakdown of the raw material to a biocrude oil containing a mixture of building block chemicals. The building block chemicals can be further refined to higher value products, often in the liquid phase, with the aid of a solvent and a solid catalyst. This project will investigate the transformation of one of those building block chemicals, 3-hydroxybutryolactone (3HBA), to several higher-value chemicals. Theoretical analysis and experimental methods will be combined to understand how the solvent influences the performance of the catalyst in promoting conversion of 3HBA to the desired products. Results of the study can be applied more generally to other bio-based chemicals to support a growing bio-refining industry relevant for the transition to renewable chemical production. The project will contribute to a highly trained workforce of experts in biomass processing, while also adding to U.S. technical prominence in biomanufacturing of chemicals.
A major goal of heterogeneous catalysis research is to identify active sites and to understand how they interact with reactants, products, and the bulk environment to facilitate chemical transformations. While most catalyst studies focus on catalyst discovery, it is often the bulk reaction environment that benefits most from redesign. The focus on solvation effects in heterogeneous catalysis has recently expanded with the trend toward liquid-phase, catalytic processing of biomass. Motivated by this shift, the project focuses on developing the scientific foundations needed for the rational design of solvent systems for catalytically processing renewable oxygenates. Specifically, the proposed research aims at understanding how the nature of the solvent microenvironment impacts activity and selectivity of ruthenium (Ru) catalysts during reductive amination of 3-HBA to form 2-amino-3-hydroxytetrahydrofuran and 3-aminotetrahydrofuran. The proposed combination of computational and experimental research is structured around (1) state-of-the-art density functional theory calculations, (2) machine learning tools for accelerating complex reaction network investigation, (3) microkinetic reactor modeling under various experimental reaction conditions, (4) vapor phase catalyst evaluation and kinetic isotope effect studies, (5) catalyst evaluations in condensed phases of water, ethanol, 1,4-dioxane, and cyclohexane, and (6) systematic correlation of experimental data with computational models through Bayesian statistical analysis. An iterative research loop is proposed, with experimental observations leading to hypotheses that motivate new computations, while computational models will rationalize experimental findings and guide new investigations. The research program includes undergraduate outreach, and research results will be integrated into undergraduate and graduate electives and the core chemical engineering curriculum at both Syracuse University and the University of South Carolina.
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.
