A Mesoionic Carbene–Pyridine Bidentate Ligand That Improves Stability in Electrocatalytic CO <sub>2</sub> Reduction by a Molecular Manganese Catalyst
Bibliographic record
Abstract
Tricarbonyl Group 7 complexes have a longstanding history as efficacious CO 2 electroreduction catalysts. Typically, these complexes feature an auxiliary 2,2′-bipyridine ligand that assists in redox steps by delocalizing the electron density into the ligand orbitals. While this feature lends to an accessible redox potential for CO 2 electroreduction, it also presents challenges for electrocatalysis with Mn because the electron density is removed from metal–ligand bonding orbitals. The results presented here thus introduce a mesoionic carbene (MIC) as a potent ligand platform to promote Mn-based electrocatalysis. The strong σ donation of the N,C-bidentate MIC is shown to help centralize the electron density on the Mn center while also maintaining relevant redox potentials for CO 2 electroreduction. Mechanistic investigation supports catalytic turnover at two operative potentials separated by 400 mV. In the low operating potential regime at −1.54 V, Mn(0) species catalyze CO 2 to CO and CO 3 2–, which has a maximum rate of 7 ± 5 s –1 and is stable for up to 30.7 h. At higher operating potential at −1.94 V, “Mn(−1)” catalyzes CO 2 to CO and H 2 O with faster turnovers of 200 ± 100 s –1, with the trade-off being less stability at 6.7 h. The relative stabilities of Mn complexes bearing MIC and 4,4′-di- tert -butyl-2,2′-bipyridine were compared by evaluation under the same electrolysis conditions and therefore elucidated that the MIC promotes longevity for CO evolution throughout a 5 h period.
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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".