Design and Discovery of Multimetallic Heterogeneous Catalysts for a Future Biorefining Industry

Project Personnel

Andreas Heyden

Principal Investigator

University of South Carolina, Columbia

Email

Gabriel Terejanu

University of South Carolina, Columbia

Email

Jesse Bond

Syracuse University

Email

Salai Ammal

University of South Carolina, Columbia

Email

Funding Divisions

Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET), Division of Chemistry (CHE)

The proposal utilizes statistical analysis to estimate uncertainties in both experimental data and theoretical calculations relating to catalytic hydrodeoxygenation of succinic acid (SUCC HDO) - an important reaction in the refining of biomass-derived chemicals to commercially valuable products. The experimental and computational methods employed - combined with statistical error analysis - provide a more accurate and powerful approach for identifying improved catalytic materials than possible by either experiments or theory alone. The approach is applicable to a broad range of catalytic applications, and could provide a blueprint for a new approach to the discovery and design of catalytic materials. The results of the study will be made available to the catalysis community via a website and software tool.
The methodology employed in the study can potentially guide materials selection and catalyst design for many applications beyond the specific catalysts and reaction demonstrated here. Rigorous standards are set for both the experimental and computational work, that when combined with statistical analysis, provide confidence heretofore lacking in the certainty with which new catalytic materials can be predicted. The selected reaction is in biomass processing and not only demonstrates application of the methods to complicated systems, but suggests potential use of the methods in both aqueous and gas-phase reactions important to renewable resources and energy sustainability.

Publications

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid
W. Yang, K. E. Abdelfatah, S. K. Kundu, B. Rajbanshi, G. A. Terejanu, and A. Heyden
6/20/2024
Modeling the Effect of Surface Platinum–Tin Alloys on Propane Dehydrogenation on Platinum–Tin Catalysts
C. H. Fricke, O. H. Bamidele, M. Bello, J. Chowdhury, G. Terejanu, and A. Heyden
7/31/2023
Propane Dehydrogenation on Platinum Catalysts: Identifying the Active Sites through Bayesian Analysis
C. Fricke, B. Rajbanshi, E. A. Walker, G. Terejanu, and A. Heyden
2/3/2022
Kinetic and Mechanistic Analysis of the Hydrodeoxygenation of Propanoic Acid on Pt/SiO2
J. Gopeesingh, R. Zhu, R. Schuarca, W. Yang, A. Heyden, and J. Q. Bond
11/4/2021
Computational Investigation of the Catalytic Hydrodeoxygenation of Propanoic Acid over a Cu(111) Surface
B. Rajbanshi, W. Yang, A. Yonge, S. K. Kundu, C. Fricke, and A. Heyden
8/26/2021
A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications
A. J. Chowdhury, W. Yang, K. E. Abdelfatah, M. Zare, A. Heyden, and G. A. Terejanu
1/21/2020
Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning
K. Abdelfatah, W. Yang, R. Vijay Solomon, B. Rajbanshi, A. Chowdhury, M. Zare, S. K. Kundu, A. Yonge, A. Heyden, and G. Terejanu
11/14/2019
Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning
A. J. Chowdhury, W. Yang, E. Walker, O. Mamun, A. Heyden, and G. A. Terejanu
11/9/2018

View All Publications

Research Highlights

Machine Learning Accelerated First-principles Study of the Hydrodeoxygenation of Propanoic Acid
G. A. Terejanu (University of North Carolina) and A. Heyden (University of South Carolina)
10/7/2024
A Neural Network Approach for Catalysis
G. A. Terejanu (University of North Carolina) and A. Heyden (University of South Carolina)
10/7/2024

Designing Materials to Revolutionize and Engineer our Future (DMREF)