Intermediate Binding Control Using Metal–Organic Frameworks Enhances Electrochemical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
In the electrochemical CO 2 reduction reaction (CO 2 RR), control over the binding of intermediates is key for tuning product selectivity and catalytic activity. Here we report the use of reticular chemistry to control the binding of CO 2 RR intermediates on metal catalysts encapsulated inside metal–organic frameworks (MOFs), thereby allowing us to improve CO 2 RR electrocatalysis. By varying systematically both the organic linker and the metal node in a face-centered cubic ( fcu ) MOF, we tune the adsorption of CO 2, pore openness, and Lewis acidity of the MOFs. Using operando X-ray absorption spectroscopy (XAS) and in situ Raman spectroscopy, we reveal that the MOFs are stable under operating conditions and that this tuning plays the role of optimizing the *CO binding mode on the surface of Ag nanoparticles incorporated inside the MOFs with the increase of local CO 2 concentration. As a result, we improve the CO selectivity from 74% for Ag/Zr- fcu -MOF-1,4-benzenedicarboxylic acid (BDC) to 94% for Ag/Zr- fcu -MOF-1,4-naphthalenedicarboxylic acid (NDC). The work offers a further avenue to utilize MOFs in the pursuit of materials design for CO 2 RR.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".