ULTRAFINE COAL CLEANING USING SELECTIVE HYDROPHOBIC COAGULATION
Bibliographic record
Abstract
ABSTRACT Studies have shown that ultrafine hydrophobic materials such as coal and graphite can be selectively coagulated and separated from hydrophilic impurities without using oily agglomerants, flocculants, or electrolytes. This process, which is referred to as Selective Hydrophobic Coagulation (SHC), has been used in laboratory tests to significantly reduce the ash and sulfur contents of several ultrafine, run-of-mine coal samples. The selective coagulation occurs at ζ-potentials significantly higher than those predicted by the classical DLVO theory due to the presence of an attractive interaction energy between hydrophobic particles such as coal. To account for this additional attraction, the interaction energies for the coal-mineral system have been calculated using an extended DLVO theory that incorporates the hydrophobic interaction energy in addition to the traditional dispersion and the electrostatic energies. The results of these theoretical calculations correlate well with separation response obtained using the SHC process. Keywords: Ultrafine coal cleaningSelective coagulationHydrophobic interactionDLVO theory Acknowledgments The authors acknowledge the financial support of the U.S. Department of Energy (Contract No. DE-AC22–90PC90174) and technical discussions with Dr. Z. Xu of the University of Alberta.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".