Effort of ionic liquids with [HSO<sub>4</sub>]<sup>‐</sup> on oxidative desulphurization of coal
Bibliographic record
Abstract
Abstract Sulphur dioxide and soot produced during coal combustion are the main air pollution sources and increased emission of PM2.5 (particle matter with an aerodynamic diameter less than or equal to 2.5 µm) in China. In this investigation, two imidazolium ionic liquids (ILs), namely, 1‐butyl‐3‐methyl imidazolium bisulphate ([C4C1im][HSO4]) and 1‐carboxymethyl‐3‐methylimidazolium bisulphate([HOOCCH2mim][HSO4]), were used to remove sulphur from coal combined with 30 % hydrogen peroxide (H2O2) based on chemically oxidative desulphurization. The experimental results indicate that H2O2 played a dominant role in removing inorganic sulphur but only partially decreased organic sulphur. The [C4C1im][HSO4]‐H2O2 solution is able to remove 47.44 % of the total sulphur in coal and nearly 100 % of the inorganic sulphur while the [HOOCCH2mim][HSO4]‐H2O2 solution can remove 16.76 % of organic sulphur with a weaker ability to reduce inorganic components. According to the FTIR spectra analysis, the results show that the proportion of –SH, –CH3, –CH2–, and‐OH declined, while −COOH increased after the IL‐H2O2 treatment due to the oxidation enhancement in the presence of ionic liquids. Sulphur element compositions were measured using XPS, and the results also show that ionic liquids are favourable for improving the oxidation of –SH, –S–, and thiophene into sulphoxide and sulphone, which were extracted during the ionic liquid phase.
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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".