Oxidative and Extractive Desulfurization of Fuel Oils Catalyzed by <i>N</i>-Carboxymethyl Pyridinium Acetate and <i>N</i>-Carboxyethyl Pyridinium Acetate Acidic Ionic Liquids: Experimental and Computational DFT Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study reports on the synthesis, characterization, and application of two acidic ionic liquids, namely, N -carboxymethylpyridinium acetate ([HO 2 CCH 2 Py][CH 3 CO 2 ] or AIL1 ) and N -carboxyethylpyridinium acetate ([HO 2 C(CH 2 ) 2 Py][CH 3 CO 2 ] or AIL2 ), as both extractants and catalysts for the oxidative and extractive desulfurization (OEDS) of model fuel oils containing heteroaromatic sulfur compounds. The structural properties of the synthesized acidic ionic liquids (ILs) were confirmed by 1 H NMR, 13 C NMR, and FT-IR spectroscopic analysis. To optimize the performance of the acidic AILs in the desulfurization process, the effects of different parameters, such as H 2 O 2 dosage, reaction time, and temperatures, were investigated. The experimental results showed that AIL1 has exceptionally high desulfurization–extraction rates, with values of 99.8%, 97.8%, and 95.4%, for DBT, BT, and 4,6-DMDBT, respectively, under the optimum conditions established. Under the same conditions, the desulfurization-extraction rates using AIL2 reached 91.6%, 87.3%, and 82.4%, respectively, for DBT, 4, 6-DMDBT, and BT. Both ionic liquids can be recycled up to 9 times without a significant decrease in their sulfur removal efficiencies. Furthermore, density functional theory (DFT) calculations were conducted to evaluate the electronic interaction energies (ΔIE) between the AILs with each of the sulfur-containing compounds and their putative oxidized products. The computational findings strongly supported the experimental outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".