A Laboratory Protocol to Assess the Electrostatic Propensity of Protective-clothing Systems
Bibliographic record
Abstract
The ability to predict the electrostatic propensity of textile systems has been an elusive goal for many years. Some small-scale test methods previously reported were found to be inadequate for certain conditions. One of the most serious problems is poor correlation between test measurements and the values of electrostatic discharges from a clothed person in real-life situations. This paper describes the development of a laboratory protocol to evaluate the electrostatic properties of clothing systems Four small-scale tests were evaluated and compared in order to select a protocol that could best assess the static propensity of protective-garment systems worn by workers in hazardous environments under dry conditions. Several two-layer fabric systems that included non-FR (non-fire-retardant) cotton, as well as thermal-protective fabrics of aramid fibre/carbon, aramid fibre/PBI (polybenzimidazole) fibre, aramid fibre/FR viscose, and FR cotton, were tested. Experiments were conducted at room temperature and 0% and 20% relative humidity (r.h.). Fabrics were evaluated in terms of both peak discharge potentials and charge decay. These peak potentials and charge decays were compared with data from human-body experiments, and significant coefficients of determination (R 2) of up to 0.99 were found when results from different tests were regressed on human-body data. It therefore seems that measuring peak discharge potentials and charge decays from charged-fabric systems and peak discharge potentials from a capacitor by using a battery of test methods can be sufficient to assess, with high accuracy, the static behaviour of garment systems in real-life conditions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".