Journal
COMMUNICATIONS CHEMISTRY
Volume 5, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42004-022-00785-2
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Funding
- ACT Programme (Accelerating CCS Technologies, Horizon 2020 Project) [299659, 294766]
- Department for Business, Energy amp
- Industrial Strategy (BEIS)
- NERC
- EPSRC Research Councils, United Kingdom
- Research Council of Norway (RCN)
- Swiss Federal Office of Energy (SFOE)
- U.S. Department of Energy
- TOTAL
- Equinor
- Swiss National Science Foundation (SNSF) [P2ELP2_195155]
- MARVEL National Centre for Competence in Research - Swiss National Science Foundation [51NF40-182892]
- Swiss National Science Foundation (SNF) [P2ELP2_195155] Funding Source: Swiss National Science Foundation (SNF)
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In this study, a joint machine learning and experimental approach is used to optimize the synthesis conditions of Al-PMOF, resulting in improved yield and crystallinity. The most important experimental variables that determine the outcome are identified through analysis of failed and partially successful experiments.
The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al-2(OH)(2)TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.
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