Aqueous CO <sub>2</sub> Reduction by a Re(bipyridine)-polypyrrole Film Deposited on Colloid-Imprinted Carbon
Bibliographic record
Abstract
Herein, we report a [Re(bipyridine)]-carbon hybrid material for efficient and selective CO 2 to CO conversion in water. The Re catalyst was incorporated into a nanoporous colloid-imprinted carbon (CIC) powder by an electrochemical polymerization method. Uniform [Re(bipyridine)]-containing polymer films were formed on the carbon surface, where the catalyst-polymer loading could be controlled by varying the polymerization parameters. Thorough pre- and post-catalysis characterization confirmed the integrity of the [Re(bpy)] CO 2 reduction catalyst. CIC| poly [Re] electrodes reduced CO 2 to CO in 0.5 M CO 2 -saturated KHCO 3 electrolyte solution at E appl. = −0.66 V versus reversible hydrogen electrode (RHE) (η = 550 mV), with initial Faradaic efficiencies for CO formation (FE CO ) between 88 and 100%. Higher catalyst loadings generally yielded higher FE CO and better long-term stability under catalytic conditions, producing CO and maintaining selectivity of above 70% for a period of at least 24 h for the hybrid material with the highest [Re(bipyridine)] polymer loading. While electrochemical analyses suggested some electron and mass transport issues on shorter timescales, these were not confirmed in long-term electrolysis. Our work highlights the great applicability of (electro)polymerization techniques in combination with nanoporous CIC to prepare hybrid materials for energy conversion.
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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 | 0.001 |
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".