An integrated method for preparing low quinoline insoluble modified pitch
Bibliographic record
Abstract
Abstract Pitches with three different softening points (SPs) were obtained from the same original coal tar purified in the laboratory by centrifuge, hot filtration, and distillation. The effect of temperature and residence time on thermal polymerization of the three types of pitch was investigated using a laboratory autoclave. The differences in the composition of the three types of pitch was established by elemental and thermogravimetric analyses. The results show that the purified coal tar with low quinoline insolubles (QI) of 1.8 g/kg (0.18 wt%) was obtained by centrifuge and hot filtration treatment at 348 K, with a 1000‐mesh filter. The coal tar recovery is approximately 800 g/kg (80 wt%). The rate of thermal polymerization is influenced by the SP of the original pitch controlled by the distillation conditions. The QI of the modified pitch decreases (from 3.5 to 1.8 g/kg, 0.35 to 0.18 wt%) in the initial reaction stages at low temperatures (below 683 K) and increases at higher temperatures during the thermal treatment. The SP, coking value (CV), and toluene insolubles (TI) of modified pitches increase monotonically with temperature. A modified pitch with a low QI of 1.8 g/kg (0.18 wt%), a SP of 366 K, a TI of 234.2 g/kg (23.42 wt%), and a CV of 485 g/kg (48.50 wt%) was prepared from the original pitch with an SP of 319 K by thermal polymerization at 683 K for 1 h.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".