Ligand-Engineered Metal–Organic Frameworks for Electrochemical Reduction of Carbon Dioxide to Carbon Monoxide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The economic feasibility of electrocatalytic carbon dioxide reduction reaction (CO2RR) 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 CO2RR. 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 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 (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/cm2. Experimental and density functional theory (DFT) results indicate that the sp2 carbon atoms in azole ligands coordinated with the metal center in 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 CALF20 and ZIF-8 have the most favorable adsorption sites at the N–sp2 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 CO2RR catalyst.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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 it