A novel method for fuel oil desulphurization by deep eutectic solvent extraction coupled with reduction using sodium borohydride
Bibliographic record
Abstract
Abstract In order to improve sulphur removal efficiency, deep eutectic solvent (DES) is first introduced into the fuel oil desulphurization process with sodium borohydride (NaBH4). Thereby, a novel method for fuel oil desulphurization by extraction coupled with reduction is proposed, where DES is used as stabilizer and extractant and NaBH4 is used as reducing agent. The nickel borides produced from NaBH4 and nickel salts were characterized using a particle size analyzer, surface area analyzer, and transmission electron microscopy. The results show that the nickel borides produced in DES have smaller particle sizes and a higher BET surface area than that in methanol/tetrahydrofuran (MeOH/THF). Sulphur removal efficiency using DES as medium was higher than that using MeOH/THF as medium. The effect of process parameters on the sulphur removal efficiency was investigated by orthogonal experiments. The results showed that the sulphur removal efficiency of dibenzothiophene can reach more than 96%. The desulphurization of dibenzothiophene should proceed over the cleavage of C−S bond to biphenyl and desulphurization reaction followed the pseudo‐first‐order reaction. The structures of the DESs remained unchanged after regeneration. In summary, the novel desulphurization method is feasible and promising.
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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) | 0.001 | 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".