The charging property of the micropowder during jet milling/electrostatic dispersion process
Bibliographic record
Abstract
Jet milling has been applied into many industries as an advanced technology in powder preparation.While the surface static electrons produced by friction between particles during grinding process and the strong adsorption property for particle size reduction will lead to agglomeration of the particles thus prepared,which offsets the advantages of micropowder to certain.Based on the Coulomb principle which states that particles carrying charges of the same sign repel each other,the electrostatic dispersion was an innovative methodology which employs electrostatic effect to annihilate agglomeration,the more the charges carried by powder is,the better the dispersion of it was.In this paper,we proposed a new method to prepare micropowder with a combination of jet milling and electrostatic dispersion techniques,and the charge-to-mass ratios of talc powders with different sizes have been measured experimentally.The results indicate that when the air pressure was lower,the charge-to-mass ratio of the talc with larger size increases with the increase of charging voltage,while it reaches the maximum at 40kV when the air pressure was larger.But the charge-to-mass ratio of the talc with smaller size increases with the increase of charging voltage all the time.Besides,the charge-to-mass ratio of talc decreases with the increase of air pressure when the charging voltage was fixed,and the charging property of powder with smaller size was better compare to larger size under the same charging condition.
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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 | 0.001 |
| 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) | 0.000 | 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".