Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling
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An efficient algorithmic workflow was developed to optimize seven process parameters of a homogeneous catalytic system with minimal experimental effort.
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References 49 · Cited by 32
Research works printed but not linked: 2
- Sen A. and Srivastava M., Regression Analysis, Springer-Verlag, New York, 1990.
- Sarkar D., Lattice, Springer-Verlag, New York, 2008.
- Authors
- Siebert, M., Krennrich, G., Seibicke, M., Siegle, A. F., & Trapp, O.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Trapp, Oliver
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 10
- Issue
- 45
- Pages
- 10466-10474
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC7012071
- OpenAlex
- W2982064992
- Cited by
- 32
- References
- 49
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 8 · 2020: 6
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Chemical Science (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Physical Sciences › Chemical Engineering › Process Chemistry and Technology › Carbon dioxide utilization in catalysis
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
- Physical Sciences › Chemical Engineering › Catalysis › Catalysts for Methane Reforming
- Keywords
- carbon dioxide reduction, dimethoxymethane, formaldehyde, ruthenium catalysis, multivariate models, machine learning, experimental design, turnover number, CO2 conversion
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