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Issue 45, 2019
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Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling

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Abstract

In times of a warming climate due to excessive carbon dioxide production, catalytic conversion of carbon dioxide to formaldehyde is not only a process of great industrial interest, but it could also serve as a means for meeting our climate goals. Currently, formaldehyde is produced in an energetically unfavourable and atom-inefficient process. A much needed solution remains academically challenging. Here we present an algorithmic workflow to improve the ruthenium-catalysed transformation of carbon dioxide to the formaldehyde derivative dimethoxymethane. Catalytic processes are typically optimised by comprehensive screening of catalysts, substrates, reaction parameters and additives to enhance activity and selectivity. The common problem of the multidimensionality of the parameter space, leading to only incremental improvement in laborious physical investigations, was overcome by combining elements from machine learning, optimisation and experimental design, tripling the turnover number of 786 to 2761. The optimised conditions were then used in a new reaction setup tailored to the process parameters leading to a turnover number of 3874, exceeding by far those of known processes.

Graphical abstract: Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling

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Supplementary files

Article information


Submitted
11 Sep 2019
Accepted
23 Oct 2019
First published
24 Oct 2019

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2019,10, 10466-10474
Article type
Edge Article

Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling

M. Siebert, G. Krennrich, M. Seibicke, A. F. Siegle and O. Trapp, Chem. Sci., 2019, 10, 10466
DOI: 10.1039/C9SC04591K

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