Comparison of three methods for the sequential extraction of lignite pretreated by ionic liquid
Bibliographic record
Abstract
Abstract In order to improve the extraction rate of coal by solvent extraction, Lianhuatang lignite (LHT) was pretreated with 1‐butyl‐3‐methylimidazolium hexafluorophosphate ionic liquid([Bmim]PF 6 ) firstly. Then, the Soxhlet extraction (SE), ultrasound‐assisted extraction (UAE), and microwave‐assisted extraction (MAE) were carried out to obtain extracts (E S1‐3 , E U1‐3 , and E M1‐3 ) and residues (R S1‐3 , R U1‐3 , and R M1‐3 ) from the Lianhuatang lignite pretreated with ionic liquid (BLHT), respectively, and CS 2 , acetone, and ethanol were used as solvents. The coal samples before and after the ionic liquid pretreatment were characterized by scanning electron microscope (SEM), Fourier‐transform infrared spectroscopy (FTIR), and thermogravimetric analyzer (TG). The residues were characterized by FTIR, and the extracts were characterized by a gas chromatograph/mass spectrometer (GC/MS). According to the results, ionic liquid can crack coal particles, increase the contact area of coal surface with solvent, and destroy part of the hydrogen bonds and intermolecular forces in coal, enabling easy extraction of the active small molecules. The macromolecular structure of coal is not destroyed in the three sequential extraction experiments. The types of extracts are similar under different methods, but the specific substances are very different. The total extraction rate by MAE was the highest, which was 48.70%. Microwave radiation can promote the extraction of oxygen‐containing substances, especially esters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".