Enhanced Electrochemical Reduction of CO <sub>2</sub> to CO upon Immobilization onto Carbon Nanotubes Using an Iron‐Porphyrin Dimer
Bibliographic record
Abstract
Abstract Electrochemical reduction of carbon dioxide (CO 2 ) is a viable solution for conversion of atmospheric CO 2 to value‐added materials such as carbon monoxide (CO). In this project, a new urea iron‐tetraphenylporphyrin‐dimer (Fe‐TPP‐Dimer) was synthesized and applied for electrocatalytic CO 2 reduction under both homogeneous and heterogeneous conditions to selectively reduce CO 2 to CO. Immobilization of the catalyst onto carbon nanotubes (CNTs) in aqueous solution resulted in remarkable enhancement of its electrocatalytic abilities, with exceptional turnover frequencies (10 s −1 ), high faradic efficiency (FE) of ∼90%, and a current density of 16 mA/cm 2 at −0.88 V vs. RHE. This project exhibits the importance of molecular design in accessing heterogeneous applications with CNTs.
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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".