The Effect of Exogenous Acid Identity on Iron Tetraphenylporphyrin‐Catalyzed CO2 Reduction
Bibliographic record
Abstract
Iron tetraphenylporphyrin (FeTPP) is a privileged electrocatalyst that displays favorable activity for the reduction of CO2 to CO. FeTPP‐catalyzed CO2 reduction is typically performed using phenol as the exogenous acid source, which promotes the rate-limiting proton coupled electron transfer (PCET). Beyond the observation that catalytic rates improve with decreasing pKa, the effects of acid identity remain largely unexplored. Herein, we describe the electrocatalytic CO2 reduction activity of FeTPP with a structurally diverse set of exogenous O−H, N−H, and C−H acids. While many of the acids surveyed here follow the expected Brønsted relationship, several deviate from this expectation. Changes in acid structure are correlated with changes in the kinetics of PCET. Rate constants obtained with the fluorinated alcohols hexafluoroisopropanol (log(kcat) = 4.54) and 2,2,2-trifluoroethanol (log(kcat) = 3.55) are several times greater than rates obtained with a similarly acidic phenol, and this class of acid affords the most favorable kinetics. The N−H acid imidazole (log(kcat) = 4.41) shows a similar rate constant as a comparably acidic fluorinated alcohol. Amides with pKa values <19 (in dimethyl sulfoxide) display similar kinetics to comparably acidic O−H donors, while less acidic amides are ~2 orders of magnitude slower relative to O−H acids of similar pKa. Consistent with their poor hydrogen bonding abilities, C−H acids show slow kinetics regardless of their pKa. An Eyring analysis performed with three acids of similar pKa suggests that acids enforcing less ordered transition states afford faster kinetics. These results emphasize that pKa is only one relevant parameter of many. Taken together, this study reveals the complexity of noncovalent interactions between exogenous acids and catalytically relevant intermediates or transition states and suggests that judicious selection of the exogenous acid is a crucial design consideration for optimizing catalytic activity.
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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 | 0.001 |
| 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".