Ligand-Engineered Metal–Organic Frameworks for Electrochemical Reduction of Carbon Dioxide to Carbon Monoxide
Bibliographic record
Abstract
The economic feasibility of electrocatalytic carbon dioxide reduction reaction (CO 2 RR) relies on developing highly selective and efficient catalysts operating at a high current density. Herein, we explore a ligand-engineering strategy involving the use of metal–organic frameworks (MOFs) and combining the desirable features of homogeneous and heterogeneous catalysts for boosting the activity of CO 2 RR. Zn-based MOFs involving two different azolate functional ligands, i.e., 1,2,4-triazole (Calgary Framework 20, CALF20) and 2-methylimidazole (zeolitic imidazolate framework-8, ZIF-8), were investigated for CO 2 RR in an alkaline flow cell electrolyzer. The highest CO partial current density of −53.2 mA/cm 2 was observed for a Zn-based MOF (CALF20). CALF20 showed the highest reported Faradaic efficiency of Zn-based MOFs for CO production (∼94% at −0.97 V versus reversible hydrogen electrode, RHE), with a turnover frequency (TOF) of 1360.8 h –1 and a partial current density of −32.8 mA/cm 2 . Experimental and density functional theory (DFT) results indicate that the sp 2 carbon atoms in azole ligands coordinated with the metal center in MOFs are the active sites for CO 2 RR due to the fully occupied 3d orbital of Zn(II) centers. Ab initio investigation shows that both azolate frameworks in CALF20 and ZIF-8 have the most favorable adsorption sites at the N–sp 2 C. Adopting the triazole ligand in CALF20 enhances the charge transfer (as compared with the diazole group in ZIF-8), which induces more electrons in the adjacent active sites at the azole ligand and facilitates *COOH formation, boosting current density and Faradaic efficiency toward CO production. This study suggests that ligand engineering in MOFs could be a viable approach to design a highly efficient CO 2 RR catalyst.
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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".