Controlling the Oxygen Reduction Selectivity of Asymmetric Cobalt Porphyrins by Using Local Electrostatic Interactions
Bibliographic record
Abstract
The development and improvement of electrocatalysts for the 4H+/4e– reduction of O2 to H2O is an ongoing challenge. The addition of ancillary groups (e.g., hydrogen bonding, Brønsted acid/base) near the active site of metal-containing catalysts is an effective way to improve selectivity and kinetics of the oxygen reduction reaction (ORR). In this regard, iron porphyrins are among the most researched ORR catalysts. Closely related cobalt porphyrin ORR catalysts can function closer to the O2/H2O thermodynamic potential, but they tend to be less selective and follow a different mechanism than for the iron porphyrins. Herein, we explore strategies to extend the ideas about ancillary groups that have been developed for iron porphyrin ORR electrocatalysts to improve the performance of the corresponding cobalt complexes. We describe a series of porphyrin electrocatalysts that are modified versions of Co(5,10,15,20-tetraphenylporphyrin), where the 2-position of one of the phenyl groups contains -NH2, -N(CH3)2, and -N(CH3)3+. Investigations using cyclic voltammetry and hydrodynamic electrochemistry show that the presence of a cationic ancillary group gives rise to a catalyst that is selective for the conversion of O2 to H2O across a wide pH range. In contrast, the other catalysts are selective for reduction of O2 to H2O at pH 0, but produce H2O2 at higher pH. The ORR rate (∼106 M–1 s–1) and selectivity of the -N(CH3)3+-modified catalyst are invariant between pH 0 and 7. Quantum chemical calculations support the hypothesis that the enhancement of selectivity can be attributed to the distinct mechanism of O2 reduction by Co-porphyrins. Specifically, the mechanism relies on anionic, peroxide-bound intermediates. While protic ancillary groups are important in the performance of iron porphyrin ORR catalysts, we suggest that electrostatic stabilizers of O2-bound intermediates are more crucial for cobalt porphyrin ORR catalysts.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".