Design and Implementation of a CO<sub>2</sub> Reduction Catalyst with an Internal Electron Transfer Mediator: Improving Turnover Frequency by More than 10-Fold
Bibliographic record
Abstract
The electrochemical upconversion of carbon dioxide (CO 2 ) is an extant chemical problem. To that end, iron porphyrins are known to readily convert CO 2 to carbon monoxide (CO). Herein, we use semiclassical electron transfer (ET) theory to design new catalysts that can supply electrons to active sites with increased rates, thereby improving CO 2 -to-CO conversion kinetics. Specifically, we report a new iron tetraphenylporphyrin complex that is modified a pyrenyl redox mediator situated ca. 12 Å from the iron ion. We demonstrate that the pyrene-based redox events occur at potentials slightly less reducing than the formal Fe I/0 redox event that affords entry to the established iron porphyrin CO 2 reduction scheme. The iron porphyrin-pyrene molecular catalyst shows CO 2 reduction rates that are between 10 and 100 times larger than related iron porphyrin electrocatalysts. We propose that the small, uphill intramolecular ET event is the origin of the substantial increase in observed CO 2 reduction rate constants. These findings show that modest uphill intramolecular ET steps can offer a promising new way to design CO 2 reduction catalysts that have improved performance. In conjunction with other leading design elements (e.g., proton delivery), the addition of redox mediators offers a strategy to further improve CO 2 reduction electrocatalyst systems.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".