Understanding the Interplay of the Brønsted Acidity of Catalyst Ancillary Groups and the Solution Components in Iron-porphyrin-Mediated Carbon Dioxide Reduction
Bibliographic record
Abstract
The rapid and efficient conversion of carbon dioxide (CO 2 ) to carbon monoxide (CO) is an ongoing challenge. Catalysts based on iron-porphyrin cores have emerged as excellent electrochemical mediators of the two proton + two electron reduction of CO 2 to CO, and many of the design features that promote function are known. Of those design features, the incorporation of Brønsted acids in the second coordination sphere of the iron ion has a significant impact on catalyst turnover kinetics. The Brønsted acids are often in the form of hydroxyphenyl groups. Herein, we explore how the acidity of an ancillary 2-hydroxyphenyl group affects the performance of CO 2 reduction electrocatalysts. A series of meso-5,10,15,20-tetraaryl porphyrins were prepared where only the functional group at the 5-meso position has an ionizable proton. A series of cyclic voltammetry (CV) experiments reveal that the complex with −OMe positioned para to the ionizable −OH shows the largest CO 2 reduction rate constants in acetonitrile solvent. This is the least acidic −OH of the compounds surveyed. The turnover frequency of the −OMe derivative can be further improved with the addition of 4-trifluoromethylphenol to the solution. In contrast, the iron-porphyrin complex with −CF 3 positioned opposite the ionizable −OH shows the smallest CO 2 reduction rate constants, and its turnover frequency is less enhanced with the addition of phenols to the reaction solutions. The origin of this effect is rationalized based on kinetic isotope effect experiments and density functional calculations. We conclude that catalysts with weaker internal acids coupled with stronger external acid additives provide superior CO 2 reduction kinetics.
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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) | 0.000 | 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".