Evolution of Catalysts Directed by Genetic Algorithms in a Plug-Based Microfluidic Device Tested with Oxidation of Methane by Oxygen
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This paper uses microfluidics to implement genetic algorithms (GA) to discover new homogeneous catalysts using the oxidation of methane by molecular oxygen as a model system. The parameters of the GA were the catalyst, a cocatalyst capable of using molecular oxygen as the terminal oxidant, and ligands that could tune the catalytic system. The GA required running hundreds of reactions to discover and optimize catalyst systems of high fitness, and microfluidics enabled these numerous reactions to be run in parallel. The small scale and volumes of microfluidics offer significant safety benefits. The microfluidic system included methods to form diverse arrays of plugs containing catalysts, introduce gaseous reagents at high pressure, run reactions in parallel, and detect catalyst activity using an in situ indicator system. Platinum(II) was identified as an active catalyst, and iron(II) and the polyoxometalate H(5)PMo(10)V(2)O(40) (POM-V2) were identified as active cocatalysts. The Pt/Fe system was further optimized and characterized using NMR experiments. After optimization, turnover numbers of approximately 50 were achieved with approximately equal production of methanol and formic acid. The Pt/Fe system demonstrated the compatibility of iron with the entire catalytic cycle. This approach of GA-guided evolution has the potential to accelerate discovery in catalysis and other areas where exploration of chemical space is essential, including optimization of materials for hydrogen storage and CO(2) capture and modifications.
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Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.
“Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [117]
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- Authors
- Kreutz, J. E., Shukhaev, A., Du, W., Druskin, S., Daugulis, O., & Ismagilov, R. F.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Daugulis, Olafs
- Ismagilov, Rustem F.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-02-11
- Volume
- 132
- Issue
- 9
- Pages
- 3128-3132
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- PMC
- PMC2861856
- OpenAlex
- W2098747348
- Cited by
- 126
- References
- 79
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 8 · 2022: 5 · 2021: 4 · 2020: 8 · 2019: 12 · 2018: 14 · 2017: 9 · 2016: 10 · 2015: 7 · 2014: 7 · 2013: 5 · 2012: 9
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Chemical Society (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (T32GM008720)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Physical Sciences › Chemical Engineering › Catalysis › Catalysis and Oxidation Reactions
- Physical Sciences › Materials Science › Materials Chemistry › Catalytic Processes in Materials Science
- Keywords
- genetic algorithm, catalyst discovery, homogeneous catalysis, methane oxidation, molecular oxygen oxidation, polyoxometalates, ligand tuning, turnover number
- MeSH terms
- Methanol, Algorithms, Catalysis, Formates, Iron, Methane, Oxidation-Reduction, Oxygen, Platinum, Microfluidic Analytical Techniques
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