Measurements of Electrostatic Charging of Powder Mixtures in a Free-fall Test Device
Bibliographic record
Abstract
Electrostatic charges generated during powder handling often adversely influence process performance. In cases where significant charges are present, accidental discharge of the accumulated charges may cause sparks, fires, even explosions, affecting process performance and causing significant safety concerns. Electrostatic charging is a complicated phenomenon, especially when handling powder mixtures of poly-disperse particles because of bi-polar charging, with small particles being charged opposite to their larger counterparts. Bipolar charging can lead to agglomeration, segregation and severe adhesion to walls or contact surfaces. Therefore, in order to characterize electrostatic properties of powders, it is desirable to measure not only the net charges on a powder, but also its polarity. In this work, a simple method is developed to investigate charge generation due to particle-particle collisions and particle-wall contact during powder handling. The experimental set-up consisted of two parallel copper plates connected to a high voltage power supply and a vibrating charging device with adjustable contacting surfaces. When subjected to a static electrical field, negatively charged particles moved to the positive side of the parallel plates, whereas positively charged particles migrated to the negative side. The separation of charged particles under investigation indicated bi-polar charging in a polydisperse powder system, with smaller particles carrying charges of polarity opposite to their larger counterparts. However, smaller particles were also found to carry two different charges from a set of fine particle-only experiments in binary powder mixtures. This was due to two different charging mechanisms: charge transfer and charge separation. In a further study with the addition of fine particles to mono-sized powders, the results indicated that the addition of fine particles helped reduce the net charges of the mixtures, with fine particles tending to carry positive charges after powder separation. When a typical antistatic agent (Larostat 519) was added to the mono-sized powders, the net charges of the mixtures decreased. Particle separation experiments revealed that this antistatic agent considerably altered the charging behavior of the host powders. This finding implies that the role of Larostat 519 in neutralizing charges differs from that of simple addition of other fine 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".