2019 — 2022 |
Goldsmith, Bryan Singh, Nirala |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Mechanistic Understanding of Electrocatalytic Bio-Oil Hydrogenation Rates: Towards a Cost-Effective Electrochemical System @ University of Michigan Ann Arbor
The transportation sector of the U.S. economy generates a large portion of greenhouse gas emissions. Increased use of renewable fuels is one option to reduce transportation-related greenhouse gas emissions and also to secure domestic, sustainable resources for fuel. One promising strategy to help meet transport needs is to synthesize transportation-grade fuels (e.g. liquid hydrocarbons) from biomass waste using renewable electricity, thereby enabling a CO2-neutral fuel source. Bio-oil hydrogenation is known to be the most capital- and energy-intensive steps for biofuel production. One promising approach to address this challenge is to use electrocatalytic hydrogenation (ECH) of biomass because it provides a sustainable method of fuel production and enables the use of renewable electricity. However, improved electrochemical systems and electrocatalysts are needed to make ECH economically competitive. This fundamental research project will address energy efficiency, product yield challenges, and economic analysis of ECH. The project will focus on the molecular pathways and reaction bottlenecks for hydrogenation reactions on metals in the aqueous phase. The research project will provide multidisciplinary training to two PhD students at the University of Michigan and enable them to conduct cutting-edge research in materials synthesis and characterization, computational modeling, and electrocatalysis. Underrepresented minority and female students will be engaged in research and outreach at both the high school and undergraduate level.
This fundamental research project will focus on the hypothesis-directed study of electrochemical hydrogenation reactions on platinum group metals and bimetallic alloys in aqueous phase. The research will advance knowledge of metals and bimetallic alloys for use in selective hydrogenation of biomass waste using renewable electricity for sustainable fuel production. The project's goal is to help engender the widespread use of electrocatalytic hydrogenation by focusing on three main areas, the adsorption of organics and hydrogen on metal surfaces, electrocatalytic hydrogenation rates on metals and bimetallics, and using a combination of theory and experiment to understand the link between adsorption and reaction rates and selectivity to predict more active and selective alloys. The team will measure and compute intrinsic reaction rates under controlled conditions on different metals, and then find correlations with adsorption energies and reaction intermediates. The project?s guiding hypothesis is that by starting with metals having moderate activity for hydrogenation and modifying to create bimetallics (e.g., Pt-alloys), one can tune the oxygenated aromatic and hydrogen adsorption energies to increase reaction rates and energy efficiency. To probe the reaction pathway and intermediates, the project will measure adsorption isotherms and use surface-enhanced Raman spectroscopy under reaction conditions to selectively probe species near the electrocatalyst surface. To complement the experimental work, the project includes density functional theory modeling of hydrogen and oxygenated aromatic adsorption energies and ECH activity at applied potentials in water. Fundamental knowledge will result of molecular-level reaction mechanisms for electrocatalytic hydrogenation systems.
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.
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0.915 |
2021 — 2025 |
Linic, Suljo [⬀] Goldsmith, Bryan Singh, Nirala |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Dmref: Machine Learning-Aided Discovery of Synthesizable, Active and Stable Heterogeneous Catalysts @ Regents of the University of Michigan - Ann Arbor
Catalytic materials have long been used to improve the efficiency and product selectivity of many processes of vital importance to chemical manufacturing, petroleum refining, and pollution control. Given the complexity of catalytic reactions, and the need for the catalyst to operate under harsh conditions in many cases, considerable development effort – particularly from industry - has gone into the design of catalyst materials that can be readily synthesized, and that maintain stable performance for long time-on-stream. Academic research efforts, in contrast, have largely focused on theoretical, computational, and experimental identification of more active and/or lower-cost catalytic materials, but with little attention to synthesizability and stability. The project creates a new catalytic materials research framework that combines the search for more active materials with screening for synthesizability and stability under reaction conditions. The added complexity is addressed through the addition of powerful machine learning (ML) approaches that augment theoretical and computational tools to yield a more complete set of properties, or “descriptors,” associated with synthesizable, highly active, and stable catalytic materials. Ultimately, the goal is to package the various discovery tools in the form of an intuitive approach that delivers optimal results for catalysis practitioners. The project builds on the widely practiced descriptor approach to catalysis research, where a descriptor of catalytic activity (e.g., adsorption energy of an adsorbate) is computed using quantum chemical Density Functional Theory (DFT) calculations on various catalyst surfaces. Research efforts extend the current approaches by developing synthesizability, stability, and activity descriptors, using ML tools to rapidly screen through these descriptors, and collaborating with experimentalists in an iterative feedback loop to examine the accuracy of the predictions and to ensure the “catalysis practitioner-friendliness” of the combined methods. The approach will be developed in two case studies focusing on bimetallic catalysts for low temperature preferential CO oxidation in the presence of H2 (CO PROX) and partial oxidation of ethylene to ethylene oxide. The project will create a computer-aided workflow and open-source tools for predicting the synthesizability, activity, and stability of catalysts. By combining ML and DFT modeling with operando experimental characterization and testing, new structure-function relations will be identified for both reactions. In doing so, ML methods will advance beyond the prediction of activity for highly idealized systems to more realistic catalytic systems under operating conditions. Predicted materials structures and compositions will be validated against open-source high-fidelity experimental datasets in a feedback discovery loop that accelerates catalyst discovery. Beyond the technical component, the project will include outreach efforts focused on student professional development, broadened science participation, and informal science communication to help create a world-class scientific workforce. Cross-disciplinary training activities at the University of Michigan (U-M) and Wayne State University (WSU) will provide graduate and undergraduate students with a foundation to continue making scientific advances throughout their careers. A Data Science for Catalysis Training Program will enable undergraduates from WSU to visit U-M during the summer to learn the basics of data science and catalysis. Underrepresented students from Detroit schools, and their parents, will engage in science outreach events hosted by team members.
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.
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0.915 |
2022 — 2024 |
Linic, Suljo (co-PI) [⬀] Goldsmith, Bryan Wang, Yixin |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Eager: Adapt: Hypotheses Generation in Heterogeneous Catalysis Using Causal Inference and Machine Learning @ Regents of the University of Michigan - Ann Arbor
With support from the Chemical Catalysis program in the Division of Chemistry (CHE), the Catalysis program from the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET), and the Office of Multidisciplinary Activities (OMA), Bryan R. Goldsmith, Yixin Wang, and Suljo Linic of the University of Michigan, Ann Arbor will work to advance knowledge generation in heterogeneous catalysis using machine learning. They will develop the methods of interpretable machine learning and causal inference to generate hypotheses and extract insight of catalytic materials that are expected to lead to more predictive models of heterogeneous catalysts. This team’s research seeks to advance machine learning methods to find descriptors of catalytic performance (e.g., activity and selectivity) and, in this way, identify structure-property relationships that have the potential guide catalyst discovery efforts for important reactions pertaining to sustainability and energy applications. Their research addresses the National Science Foundation focus area of “AI for Concept Discovery”, and will benefit many areas beyond catalysis, such as enabling researchers to apply state-of-the-art machine learning algorithms to generate hypotheses and find new electrolytes or systems for renewable energy storage applications. This team will provide interdisciplinary training at the nexus of machine learning, statistics, and catalysis, which will help train an AI-aware workforce. They also will use a summer research internship program as a mechanism to broaden participation in AI-related STEM fields.<br/><br/>Physically transparent models that can accurately quantify chemical and physical interactions between a surface of a material and adsorbate molecules (i.e., chemisorption) are crucial in many fields of chemistry and materials science. It has been known for a long time, going all the way back to the early 1900s, that chemisorption energies of adsorbates at gas/solid and liquid/solid interfaces are predictive descriptors of catalytic performance. There is a need to develop predictive theories of chemisorption that give insight into the underlying physical principles that govern chemical interaction at catalytic interfaces. Physically transparent and simple models that can accurately relate electronic and geometric features of a surface to its chemical properties and catalytic activity can allow us to rapidly predict or intuit which materials have specific chemical and catalytic features required for a particular application. This team will develop interpretable machine learning (i.e., models that can give researchers meaningful physical insights) and causal inference tools to generate hypotheses and extract insight that could lead to more predictive chemisorption models of heterogeneous catalysts. The team will focus on two state-of-the-art approaches; namely, generalized additive models and causal representation learning, and will advance these methods to understand adsorption of molecules on dilute alloy surfaces. A major goal is to identify causal links between electronic-structure, geometry, and chemisorption for dilute alloy catalysts. Although the focus here is on chemisorption and chemical catalysis on alloys, the developed methods are expected to be seamlessly integrated for use in other fields.<br/><br/>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.
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0.915 |