Porphyrin Aggregation under Homogeneous Conditions Inhibits Electrocatalysis: A Case Study on CO₂ Reduction
Bibliographic record
Abstract
Metalloporphyrins are widely used as homogeneous electrocatalysts for transformations relevant to clean energy, environmental remediation, and sustainable organic synthesis. It is common knowledge that metalloporphyrins are prone to aggregation due to π–π stacking of their planar structures, but surprisingly the influence of aggregation on homogeneous electrocatalytic performance has not been investigated previously. Herein, we present three structurally related iron meso-phenylporphyrins whose aggregation properties are expected to be different in commonly used N,N-dimethylformamide (DMF) electrolyte. Both spectroscopy and light scattering are used to provide evidence of extensive porphyrin aggregation under conventional electrocatalytic conditions. Using the electrocatalytic reduction of CO₂ to CO as a test reaction, cyclic voltammetry reveals an inverse dependence of kinetics on catalyst concentration for all three porphyrins. The inhibition at higher catalyst concentrations extends to bulk performance, where up to 75% of the catalyst at 1 mM is inactive compared to at 0.25 mM. We additionally report how aggregation is perturbed by organic additives, axial ligands, and porphyrin redox state. Periodic boundary calculations are used to gain additional insights into aggregate stability as a function of metalloporphyrin structure. Finally, we generalize the aggregation phenomenon by surveying metalloporphyrins with different metals and substituents. The takeaways of this study are that homogeneous metalloporphyrin catalysts can aggregate severely even in well-solubilizing organic electrolytes, that aggregation can be easily modulated through choice of experimental conditions, and that the extent of aggregation must be considered for accurate catalytic benchmarking.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".