| NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
| Recipient: |
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| Initial Amendment Date: | August 4, 2020 |
| Latest Amendment Date: | August 4, 2020 |
| Award Number: | 2016225 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Robert McCabe
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
| Start Date: | September 1, 2020 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $375,369.00 |
| Total Awarded Amount to Date: | $375,369.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3100 MARINE ST Boulder CO US 80309-0001 (303)492-6221 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
572 UCB Boulder CO US 80303-1058 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Catalysis |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Emission of carbon dioxide into the atmosphere is the major driver of climate change, and the path to a sustainable future will rely heavily on removing carbon dioxide from the air and either storing it or converting it into fuels or other valuable chemicals. One promising route for accomplishing this is to use electricity to drive the reaction of carbon dioxide with water to produce new chemicals. This reaction can occur on the surfaces of various materials with appropriate catalytic properties. Recently, an interesting family of materials known as Chevrels were shown to convert carbon dioxide to fuels. However, despite these promising initial results, this family of materials remains relatively unstudied and the efficiency of this reaction still needs substantial improvement to become economical. The objective of this work is to identify new Chevrel materials of the vast number of possible Chevrels that are capable of effectively converting carbon dioxide into valuable products. Identification of superior materials for this reaction could provide a major step towards reducing the level of carbon dioxide in the atmosphere and transitioning towards a sustainable future.
Electrocatalytic production of methanol and C1+ products (reduction products with > 1 carbon atom) remains a significant materials discovery challenge due to the poor selectivity and/or high overpotentials of existing electrochemical CO2 reduction (eCO2R) catalysts. Intercalated Chevrels (MyMo6X8, M = metal, X = S, Se, Te) are a promising but relatively unexplored class of materials that, like perovskites, provide a highly tunable framework for materials design and discovery with a broad range of potential applications. Furthermore, they were recently demonstrated to produce methanol selectively from CO2, suggesting that intercalated Chevrel phase materials may also be a relatively unexplored class of promising electrocatalysts that can be tuned for catalytic performance. The objective of this project is to computationally analyze and guide the design and accelerated discovery of new Chevrel phase electrocatalysts for efficient and selective CO2 conversions to valuable products. The strategy for accomplishing this goal is to 1) use state-of-the art computational quantum modeling tools to determine the mechanism of eCO2R on Chevrel surfaces in solvent and under an applied bias and 2) develop machine learned descriptors of catalyst stability, selectivity, and activity that enable the rational, high-throughput discovery of new high-performance Chevrel electrocatalysts that employ earth-abundant elements for economically-competitive CO2 conversions to valuable products. This research aligns closely with the topic areas of interest to this program, including renewable energy related catalysis, electrocatalysis, closing the carbon cycle, conversion of CO2, new catalyst designs and materials, basic understanding of catalyst materials and mechanisms and advances in tools for computational catalysis.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This NSF-supported project aimed to discover new materials for electrocatalysis, a process critical for reducing carbon dioxide emissions and advancing clean energy technologies. By integrating cutting-edge machine learning techniques with quantum chemical modeling, we initially targeted Chevrel phase materials as promising candidates for electrocatalysis. The project also included an unfunded collaboration with Prof. Jesús Velázquez at UC Davis, who contributed experimental validation of our computational predictions.
Intellectual Merit
The research achieved significant advances in the discovery and understanding of materials for electrocatalysis:
- Development of Machine Learning Tools: A new machine learning descriptor was created to predict the stability and synthetic methods for Chevrel phase materials. This enabled efficient screening of potential electrocatalysts, significantly reducing the time required for traditional computational methods.
- Novel Mechanisms for Catalysis: We predicted and experimentally validated a biomimetic mechanism for nitrogen (N₂) reduction on iron-based Chevrel phases. This work not only highlighted the versatility of Chevrel materials but also provided insights into their potential applications in electrocatalytic ammonia synthesis.
- Expanded Scope: While initially focused on CO₂ reduction, the project expanded to explore other critical reactions, including N₂ reduction, hydrogen evolution, and water splitting. This broader approach yielded a deeper understanding of material behavior at electrified interfaces.
- Database Development: The research resulted in two extensive databases: one containing over 2,000 electrocatalyst materials and reactions, and another with approximately 60,000 perovskite oxide materials. These publicly available resources are designed to support future research and the training of machine learning models for material discovery for electrocatalysis.
The research culminated in 14 published papers, one manuscript under review and another in preparation, contributing significantly to the fields of computational chemistry and machine learning-guided materials discovery.
Broader Impacts
This project provided substantial educational and professional development opportunities. Six PhD students were trained through the project, with four successfully completing their degrees. Students gained experience in advanced computational methods, machine learning, and professional skills, such as presenting research, writing manuscripts, and preparing patent applications.
The collaboration with Prof. Velázquez fostered mentorship of underrepresented minority students, promoting diversity in STEM fields. The databases created through this research will serve as valuable tools for researchers worldwide, accelerating the development of sustainable technologies for CO₂ reduction and beyond.
Additionally, the findings have been disseminated through publications, conference presentations, and seminars, ensuring broad access to the outcomes. A database of electrocatalyst properties is being hosted by the Department of Energy’s National Renewable Energy Laboratory, creating a resource for academic and industrial researchers focused on electrification of the chemical industry.
Societal Relevance
This work addresses global challenges by advancing technologies that can mitigate climate change and support the transition to a sustainable energy future. By enabling the discovery of efficient electrocatalysts, the project contributes to the broader effort to decarbonize industrial processes and energy systems.
This project exemplifies the transformative potential of combining machine learning with computational quantum chemical methods to accelerate materials discovery, with lasting impacts on science, education, and society.
Last Modified: 01/24/2025
Modified by: Charles B Musgrave
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