Electrochemical Synthesis of Urea from Carbon Dioxide and Nitrite at Cobalt Phthalocyanine-Ion Liquid Electrodes
Bibliographic record
Abstract
Electrochemical coreduction of carbon dioxide and nitrogen oxyanion/oxide pollutants are attractive processes for simultaneous environmental remediation and sustainable production of urea. The development of suitable technology requires catalysts and electrodes that provide higher efficiencies by decreasing the overpotential required and increasing the faradaic efficiency. Electrode design is a key element in this process through which the environment of the catalyst can be manipulated to optimize activity and selectivity. Here, ionic liquids have been used to control the coreduction of carbon dioxide and nitrite at a cobalt phthalocyanine catalyst. Increasing the hydrophobicity of the catalyst layer with a mixture of 1-butylpyridinium hexafluorophosphate and trihexyltetradecylphosphonium bis(trifluoromethylsulfonyl)imide was found to increase the faradaic efficiency for urea formation to 27% at the lowest overpotential (−0.064 V vs RHE), from 3% for a Nafion binder. Modulation of the electronic structure, arrangement (aggregation vs adsorption on the carbon support) and/or mobility (via solubilization) of the CoPc catalyst appear to play a role in determining the rate and faradaic efficiency of urea production. Combining the CoPc catalyst with a carbon supported Cu cocatalyst increased the rate of urea production by 195% at –0.064 V.
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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 | 0.001 |
| 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".