The Pattern of Hydroxyphenyl-Substitution Influences CO<sub>2</sub> Reduction More Strongly than the Number of Hydroxyphenyl Groups in Iron-Porphyrin Electrocatalysts
Bibliographic record
Abstract
The development of catalysts that can convert carbon dioxide (CO 2 ) to useful reduced products is a pressing and ongoing challenge. Refinement of the designs of molecular electrocatalysts is of great interest, especially for meso -tetraarylmetalloporphyrins. Iron porphyrins with hydroxyphenyl groups situated near the active site are good electrocatalysts, and herein, we systematically explore how the position and number of meso- 2,6-dihydroxyphenyl groups on iron porphyrins influences CO 2 -to-CO conversion. A series of five iron porphyrins with 2,6-dihydroxyphenyl groups systematically placed at the 5, 10, 15, and 20 porphyrin positions were prepared. The isomer with 5,15-bis(2,6-dihydroxyphenyl) substitution was a superior catalyst for CO 2 reduction electrocatalysis in N,N -dimethylformamide solvent. To our surprise, the previously reported tetrakis(2,6-dihydroxyphenyl)porphyrin iron complex was not the best performing catalyst. We use density functional calculations to explore the factors that distinguish each of the catalysts and show how calculated Fe–C vibrational frequencies are related to observed electrochemical properties and catalyst kinetics. A corresponding analysis of optical spectra and relative reduction potentials illustrates the relationship between the placement of dihydroxyphenyl groups and catalyst performance. We conclude that substituents at the 5 and 15 positions are best at improving catalyst performance, leaving other parts of porphyrin macrocycles open for other modifications.
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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".