By Nicola Nosengo, NCCR MARVEL
Photocatalysis, the use of light to drive and accelerate chemical reactions, could be a game changer in the race towards greener chemistry and sustainable energy. It could allow to harness solar energy to split water into oxygen and hydrogen, and then use the latter as an energy vector or for a more sustainable production of chemicals, such as ammonia. Or it could be used to mimic natural photosynthesis and remove CO2 from the atmosphere, thus mitigating climate change.
That future depends on finding suitable photocatalytic materials that can drive the reaction, which is still an ongoing search. In principle, covalent organic frameworks (COF) are great candidates. They are porous crystalline materials, based on abundant and non-toxic elements bound by covalent bonds, and we know that many of them have properties, such as visible light absorption and high charge-carrier mobility that make them promising for photocatalysis. “COFs are interesting first of all because they are modular materials,” says Beatriz Bueno Mouriño, a doctoral assistant in Berend Smit’s laboratory at EPFL, part of NCCR MARVEL. “You can choose different building blocks and assemble them in many different configurations. And many of them are two-dimensional materials, among which it is more likely to find a band-like behavior with more mobile charge carriers”. Finally, being based on covalent bonds they are expected to be more stable when exposed to light than other candidates, such as metal-organic frameworks (MOFs).
But the modular nature of COFs also means that there are potentially thousands of them, and scientists need a way to search of the best candidates before conducting experiments.
Now a new study by Smit’s group introduces a new computational framework that allows to screen large numbers of COFs in a fast and efficient way, to pre-select the best candidates for specific photocatalytic applications, such as water splitting. The key innovation is the selection and use of some tailored descriptors that were obtained from post-processing of density functional theory (DFT) calculations at low level of theory to cost-effectively assess key aspects of photocatalysis.
“We thought of some characteristics associated with the fundamental steps of photocatalysis and that we wanted to take into account,” says Mouriño, who is first author of the article published in Advanced Functional Materials. “Among them we chose to consider band gap, which is important for visible light, and the position of the conduction and valence bands after alignment to a vacuum potential, which allows us to compare with the redox potential of the reaction. We also considered the charge carrier effective masses to qualitatively assess their mobility, and the possibility for charge recombination evaluated by orbital analysis from charged doublets, that is, electron and hole injection.” In particular, the analysis focused on materials suitable for the water splitting reaction, but can be easily expanded to other photocatalytic reactions.
The AiiDA-based workflow was applied to a set of 419 COFs for which there are reported experiments, extracted from the CURATED COFs database. Because COFs have unit cells made of many atoms, the scientists needed a strategy to achieve accuracy without a high-fidelity simulation, which would have been computationally too expensive. The key trick was to compute everything using a “lower-fidelity” method, PBE, and then adjust afterwards to the more accurate PBE0 level, relying on correlations between the PBE and PBE0 versions of the charge recombination and energy-based descriptors that were tested beforehand on a subset of materials
“In doing this, we applied previous knowledge that we had developed with MOFs and applied it to COFs, thinking that being purely organic materials we would get a better correlation, and it worked” says Andres Ortega-Guerrero, a Postdoc in Smit’s group and a co-author of the article.
B. Mourino, K. M. Jablonka, A. Ortega-Guerrero, B. Smit, "In Search of Covalent Organic Framework Photocatalysts: A DFT-Based Screening Approach", Adv. Funct. Mater. 2301594 (2023). DOI: 10.1002/adfm.202301594.
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