Underevaluated Solvent Effects in Electrocatalytic CO<sub>2</sub> Reduction by Fe<sup>III</sup> Chloride Tetrakis(pentafluorophenyl)porphyrin
Bibliographic record
Abstract
Abstract FeIII chloride tetrakis(pentafluorophenyl)porphyrin (1) is known to have poor electrocatalytic activity for the CO2‐to‐CO conversion in dimethylformamide. In this work, we re‐examined Fe porphyrin 1 as a CO2 reduction catalyst in various solvents. Our results show that 1 displays fairly high electrocatalytic CO2‐to‐CO activity in acetonitrile with a turnover frequency (TOF) up to 4.2×104 s−1. On the other hand, 1 shows a modest activity in propylene carbonate, and is inefficient to catalyze CO2 reduction in benzonitrile, dimethylformamide, and tetrahydrofuran. Several solvent effects were considered, but none of these effects alone can explain the observed large activity difference of 1 for CO2 reduction in these solvents. Based on the results, it is suggested that more care should be paid when comparing different CO2 reduction catalysts because solvent effects are significant and are underevaluated.
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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 | 0.001 |
| 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".