A Mesoionic Carbene-Pyridine Bidentate Ligand Improves Stability in Electrocatalytic CO2 Reduction by a Molecular Manganese Catalyst
Bibliographic record
Abstract
Tricarbonyl group 7 complexes have a longstanding history as efficacious CO2 electroreduction catalysts. Typically, these complexes feature an auxiliary 2,2-bipyridine ligand which assists in redox steps by delocalizing electron density into the ligand-orbitals. While this feature lends to accessible redox potential for CO2 electroreduction, it also presents challenges for electrocatalysis with manganese, since the electron density is removed from metal-ligand bonding orbitals. The results presented here thus introduce mesoionic carbene (MIC) as a new potent ligand platform to promote Mn-based electrocatalysis. The strong σ-donation of the N,C-bidentate MIC is shown to help centralize electron density on the Mn-center while also maintaining relevant redox potentials for CO2 electroreduction. Mechanistic investigation supports catalytic turnover at two operative potentials separated by 400 mV. In the low overpotential regime, Mn(0) species catalyze CO2 to CO and CO32- with a maximum rate of 7 ± 5 s-1 and is stable for up to 30.7 h. At higher overpotential, “Mn(-1)” catalyzes CO2 to CO and H2O with faster turnovers of 200 ± 100 s-1 with the trade-off of being less stable at 6.7 h. The relative stability of Mn-complexes bearing MIC and 4-4’-diterbutyl-2,2’-bipyridine was compared by evaluating under the same electrolysis conditions, and therefore elucidated that the MIC promotes longevity for CO evolution through-out 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".