Extractive desulfurization of crude petroleum oil and liquid fuels using trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate ionic liquid
Bibliographic record
Abstract
Abstract Increasing environmental concerns have led to the development of alternative methods for the desulfurization of petroleum crude oil and liquid fuels. Phosphonium-based ionic liquids (PILs) have recently demonstrated promising potential for effective extractive desulfurization (EDS). The present study focuses on the synthesis and application of trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate [THTDP][D2EHP] for EDS of synthetic model fuels and real crude oils. The molecular confirmation and thermal stability of [THTDP][D2EHP] were investigated using FTIR and TGA analyses. In addition, the conductivity, solubility, and viscosity of the synthesized ionic liquid (IL) were analyzed. The impact of reaction time, temperature, and sulfur compounds, such as thiophene, benzothiophene, and dibenzothiophene (DBT), on the desulfurization efficiency from synthetic fuels was also investigated. The results indicated up to 63 and 57 % sulfur removal from DBT-based model fuels and Iranian crude oil, respectively. The optimum extraction conditions were found as 1:1 IL/fuel mass ratio, 35 °C, and 30 min. The findings of this study provide valuable insights into the synthesis and utilization of PILs as promising solvents for extractive desulfurization of crude oil and liquid fuels.
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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 | 0.000 |
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".