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 (CO2RR) relies on the development of 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 CO2RR. Zn-based MOFs involving two different azolate functional ligands i.e., 1,2,4-triazole (Calgary Framework-20, CALF-20) and 2-methylimidazole (zeolitic imidazolate framework-8, ZIF-8) were investigated for CO2RR in an alkaline flow cell electrolyzer. The highest CO partial current density of 53.2 mA/cm2 was observed for a Zn-based MOF (CALF-20). CALF-20 showed the highest reported Faradaic efficiency of Zn-based MOFs for CO production (ca. 94% at -0.969 V vs. reversible hydrogen electrode, RHE), with a TOF of 1360.8 h-1 and a partial current density -32.8 mA/cm2. Experimental and density functional theory (DFT) results indicate that the sp2 carbon atoms in azole ligands coordinated with the metal center in the MOFs are the active sites for CO2RR, due to the fully occupied 3d orbital of Zn(II) centers. Ab initio investigation shows that both azolate frameworks in CALF-20 and ZIF-8 have most favorable adsorption sites at the N−sp2 C. Adopting the triazole ligand in CALF-20 enhances the charge transfer (as compared with diazole group in ZIF-8), which induces more electrons in the adjacent active sites at the azole ligand and facilitates *COOH formation, boosting the current density and Faradaic efficiency towards CO production. This study suggests that ligand engineering in MOFs could be a viable approach to design highly efficient CO2RR 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".