Hydrothermal dewatering of lignite water slurries: Part 2 surface properties and stability
Bibliographic record
Abstract
Abstract In this paper, the mechanism of the stability of lignite lignite water slurry after hydrothermal dewatering/treatment (HTD) was studied by the measurement of the yield stress and the fractal dimension of the slurry. The effects of HTD on the surface properties of low rank coal were characterized by the contact angle, zeta potential, and Fourier transform infrared (FTIR) spectroscopy. After HTD, the hydrophobic attraction between coal particles was found to increase, as shown by the increase of the contact angle, and the electrical double layer repulsion was found to decrease, due to the decrease of zeta potential. These effects lead to an increase on the inter‐particle attractions, which is also proven by the theoretical calculations on the interaction energy using the extended DLVO theory. The stability measurements show that the low rank coal water slurry (CWS) becomes more stable after HTD, which is probably due to the formation of a network structure (soft sediment) by coal aggregating across the whole volume of the slurry to prevent coal particles from settling down. The network structure (soft sediment) in coal water slurry after HTD was verified and quantified by the yield stress and fractal dimension. Our findings indicated that the static stability of coal water slurry may be acquired by the network structure of the aggregated coal particles.
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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) | 0.000 | 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 | 0.000 |
| 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".