Fuel desulfurization using ionic liquids –An updated review
Bibliographic record
Abstract
Environmental and human safety has become an important subject of research interest in petroleum chemistry and environmental catalysis. The presence of sulfur-containing compounds in fuel oil is an overwhelming problem in the petrochemical industries due to regulations for environmental protection. The emitted toxic gases from the combustion of fossil fuels result in air contamination that can damage the human respiratory system, cause acid rain, and negatively affect refinery equipment. Therefore, fuel desulfurization becomes necessary to remove sulfur and its derivatives from fuel oil. Recently, ionic liquids (ILs) have been identified as good candidates for many industrial applications. They are widely known for their potential as designer solvents due to fascinating properties such as low volatility, high tunability, and high thermal stability. However, their general environmental impact varies significantly depending on their specific chemical compositions with some ILs exhibiting toxicity or poor biodegradability. The present study comprehensively reviews the recent achievements and applications of ILs in fuel oil desulfurization, addressing the mechanisms of the conversion process. The practicality and reusability of ILs for fuel oil desulfurization have been reported, and finally, the conclusion and future research prospects in this area have been suggested. This review is anticipated to provide insights into fuel refinement and purification technologies through the sustainable application of ILs for clean fuel generation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".