Electrochemical coreduction of nitrite and CO₂ in an ionic liquid system
Bibliographic record
Abstract
Environmental concerns and demand for sustainability drive investigations of electrochemical reduction of CO₂, N₂, and nitrogen pollutants. The simultaneous reduction of CO₂ and nitrite to generate urea tackles multiple issues while producing valuable products. Moreover, incorporating ionic liquids (ILs) improves catalytic performance and selectivity. This study investigates the performance of various catalysts, including cobalt and iron phthalocyanine, as well as Cu, Pd, Ir, MoS₂, TiO₂, and Rh nanoparticles, and graphene nanoplatelets in terms of urea production rates and yields. When a cobalt phthalocyanine catalyst was combined with a mixture of 1-butylpyridinium hexafluorophosphate (BuPyPF₆) and trihexyltetradecylphosphonium bis(trifluoromethylsulfonyl)imide (P₆₆₆₁₄NTf₂), the hydrophobic nature of the catalyst layer increased, resulting in higher faradaic efficiency (25% at –0.064 V vs RHE). Combining a commercial carbon-supported Cu catalyst with CoPc proved effective in increasing urea production rates, although it led to a decrease in faradaic efficiency. However, the application of carbon black as the supporting layer proved advantageous when graphene and TiO₂ as the catalyst supports were used. TiO₂, in particular, showed promise as both the catalyst and supporting material, achieving an impressive 71% urea yield when combined with CoPc. Fe (III) tetrasulfophthalocyanine, in conjunction with the mixed IL binder, exhibited a high urea production rate but low yield.
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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 | 0.001 | 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".