Pendant Catechol Group Improves the Performance of Iron Porphyrin CO<sub>2</sub> Reduction Catalysts
Bibliographic record
Abstract
In this work, we prepared chloroiron 5-(2,3-dihydroxyphenyl)-10,15,20-triphenyl porphyrin (Fe(Cat)TPP) and investigated its properties for CO 2 reduction. Iron porphyrins make up a class of compounds that are known to efficiently convert CO 2 to carbon monoxide (CO). There exist many different porphyrin derivatives that reduce CO 2 with some leading examples, including structures that place hydrogen bond donors and/or proton donors near the iron ion active site. Here, the presence of an internal H-bond in the 2,3-dihydroxyphenyl group in Fe(Cat)TPP increases the observed CO 2 reduction rate constants by a factor of 10 with respect to the parent chloroiron-5,10,15,20-tetraphenyl porphyrin and a factor of 3 with respect to a porphyrin with only one hydroxyphenyl (i.e., chloroiron 5-(2-hydroxyphenyl)-10,15,20-triphenyl porphyrin). The presence of the internal H-bond is proposed to facilitate the proton-coupled electron transfer process of carbon–oxygen bond breaking, which has been established as the rate-limiting step in CO 2 -to-CO conversion. The Fe(Cat)TPP molecule, and those like it, are important to improving designs of molecular electrocatalysts. The ongoing development of platforms that can rapidly and selectively mediate the electrochemical transformation of carbon dioxide (CO 2 ) to valorized products is a great technical challenge.
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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".