Promises of MOF‐Based and MOF‐Derived Materials for Electrocatalytic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Abstract Electrocatalytic CO 2 reduction (ECR) powered by renewable electricity is a promising technology to mitigate carbon emissions and lessen the dependence on fossil fuels toward a carbon‐neutral energy cycle. Metal–organic frameworks (MOFs) and their derivatives, due to their excellent intrinsic activity, have emerged as promising materials for the ECR to high‐demand products. However, challenges such as unsatisfactory energy efficiency, selectivity, and relatively low production rates hinder their industrial scalability. Here, a comprehensive and critical review is presented that summarizes the state‐of‐the‐art progress in MOF‐based and MOF‐derived CO 2 electroreduction catalysts from design and functionality perspectives. The fundamentals of CO 2 reduction reaction (CO 2 RR) over heterogeneous catalysts, reaction mechanisms, and key challenges faced by ECR are described first to establish a solid foundation for forthcoming in‐depth analyses. MOF's building blocks, properties, and shortcomings pertinent to ECR including low conductivity and stability, are systematically discussed. Moreover, comprehensive discussions are provided on MOF‐based and MOF‐derived catalysts design, fabrication, characterization, and CO 2 RR activity to pinpoint the intricate structure‐property‐performance relationship. Finally, promising recommendations are put forward for enhancing MOF electrocatalysts activity, selectivity, and durability. This work may serve as a guideline for developing high‐performance MOF‐related catalysts for CO 2 RR, benefiting researchers working in this growing and potentially game‐changing area.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".