Distribution of Electric Potential at the Surface of Corona-Charged Polypropylene Nonwoven Fabrics After Neutralization
Bibliographic record
Abstract
Accumulation of electric charges on insulating surfaces is frequently at the origin of severe electrostatic hazards. The best solution to this problem is to neutralize these charges by the use of ac corona discharges. The aim of this work is to evaluate the efficiency of this process in the specific case of fibrous electrets, by measuring the repartition of the electric potential at the surface of nonwoven polypropylene fabrics before and after neutralization. The samples were charged for 10 s,in ambient air, using a triode-type corona electrode system of positive polarity. In all the experiments, the neutralization was performed 180 s after the charging process. In some of them, the neutralization electrode (tungsten wire with a diameter of 0.3 mm) was fixed at a given distance (50 mm) above the samples and energized from an ac voltage amplifier (model 30/20A, Trek, Inc., Medina, NY, USA). In other experiments, the samples moved at constant speed (3 cm/s) in the ac corona discharge zone generated by the neutralization electrode. The experimental results show that the efficiency of neutralization (expressed as the relative reduction of the average potential measured at the surface of the sample) depends on the amplitude and the frequency of the sinusoidal high voltage, as well as on the charging time and the neutralization duration. Better effects were recorded in the case of the samples that moved through the corona discharge zone.
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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 | 0.000 |
| 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".