Exchange Coupling Determines Metal-Dependent Efficiency for Iron- and Cobalt-Catalyzed Photochemical CO2 Reduction
Bibliographic record
Abstract
Catalyst platforms that promote multielectron charge delocalization offer an attractive approach to achieving the CO2 reduction reaction (CO2RR) with selectivity over the competing hydrogen evolution reaction (HER). Here, we show the importance of metal-ligand exchange coupling as an example of charge delocalization that can determine efficiency for photocatalytic CO2RR. A comparative evaluation of iron and cobalt complexes supported by the redox-active ligand tpyPY2Me establishes that the two-electron reduction of [Co(tpyPY2Me)]2+ ([Co]2+) occurs at potentials 770 mV more negative than the [Fe(tpyPY2Me)]2+ ([Fe]2+) analog by maximizing exchange coupling in the latter compound. The positive shift in reduction potential promoted by metal-ligand exchange coupling drives [Fe]2+ to be among the most active and selective molecular catalysts for photochemical CO2RR reported to date, maintaining up to 99% CO product selectivity with total turnover numbers (TON) and initial turnover frequencies (TOF) exceeding 30,000 and 900 min–1, respectively. In contrast, [Co]2+ shows much lower CO2RR activity, reaching only ca. 600 TON at 83% CO product selectivity under similar conditions accompanied by rapid catalyst decomposition. Spin density plots of the two-electron reduced [Co]0 complex implicate a paramagnetic open-shell doublet ground state compared to the diamagnetic open-shell singlet ground state of reduced [Fe]0, rationalizing the observed negative shift in two-electron reduction potentials from the [M]2+ species and lowered CO2RR efficiency for the cobalt complex relative to its iron congener. This work emphasizes the contributions of multielectron metal-ligand exchange coupling in promoting effective CO2RR and provides a starting point for the broader incorporation of this strategy in catalyst design.
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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".