Artificial photosynthesis with metal and covalent organic frameworks (MOFs and COFs): challenges and prospects in fuel‐forming electrocatalysis
Bibliographic record
Abstract
Mimicking photosynthesis in generating chemical fuels from sunlight is a promising strategy to alleviate society's demand for fossil fuels. However, this approach involves a number of challenges that must be overcome before this concept can emerge as a viable solution to society's energy demand. Particularly in artificial photosynthesis, the catalytic chemistry that converts energy in the form of electricity into carbon‐based fuels and chemicals has yet to be developed. Here, we describe the foundational work and future prospects of an emerging and promising class of materials: metal‐ and covalent‐organic frameworks (MOFs and COFs). Within this context, these porous and tuneable framework materials have achieved initial success in converting abundant feedstocks (H2O and CO2) into chemicals and fuels. In this review, we first highlight key achievements in this direction. We then follow with a perspective on precisely how MOFs and COFs can perform in ways not possible with conventional molecular or heterogeneous catalysts. We conclude with a view on how spectroscopically probing MOF and COF catalysis can be used to elucidate reaction mechanisms and material dynamics throughout the course of reaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".