Intermediate Binding Control Using Metal–Organic Frameworks Enhances Electrochemical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
In the electrochemical CO2 reduction reaction (CO2RR), 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 CO2RR intermediates on metal catalysts encapsulated inside metal–organic frameworks (MOFs), thereby allowing us to improve CO2RR electrocatalysis. By varying systematically both the organic linker and the metal node in a face-centered cubic (fcu) MOF, we tune the adsorption of CO2, 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 CO2 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 CO2RR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".