Efficacy of surface sampling methods for different types of beryllium compounds
Bibliographic record
Abstract
The objective of the research work was to evaluate the efficiency of three different sampling methods (Ghost Wipe™, micro-vacuum, and ChemTest®) in the recovery of Be dust by assessing: (1) four Be compounds (beryllium acetate, beryllium chloride, beryllium oxide and beryllium aluminium), (2) three different surfaces (polystyrene, glass and aluminium) and (3) inter-operator variation. The three sampling methods were also tested on site in a laboratory of a dental school for validation purposes. The Ghost Wipe™ method showed recovery ranging from 43.3% to 85.8% for all four Be compounds and for all three quantities of Be spiked on Petri dishes, while recovery with the micro-vacuum method ranged from 0.1% to 12.4%. On polystyrene dishes with 0.4 µg Be, the recovery ranged from 48.3% to 81.7%, with an average recovery of 59.4% for Operator 1 and 68.4% for Operator 2. The ChemTest® wipe method with beryllium acetate, beryllium chloride, and AlBeMet® showed analogous results that are in line with the manufacturer's manual, but collection of beryllium oxide was negative. In the dental laboratory, Ghost Wipe™ samplings showed better recovery than the micro-vacuum method. The ratios between the recovered quantities of Be in each location where the Ghost Wipe™ was tested differed substantially, ranging from 1.45 to 64. In the dental laboratory, a faint blue color indicating the presence of Be was observed on the ChemTest® wipes used in two locations out of six. In summary, the Ghost Wipe™ method was more efficient than micro-vacuuming in collecting the Be dust from smooth, non-porous surfaces such as Petri dishes by a factor of approximately 18. The results obtained on site in a dental laboratory also showed better recovery with Ghost Wipes™. However, the ratio of Be recovered by Ghost Wipes™ versus micro-vacuuming was much lower for surfaces where a large amount of dust was present. Wet wiping is preferred over micro-vacuuming for beryllium forms, but this conclusion probably applies to the ultra-low particulate loading levels (0.4 micrograms or less) which was tested in this study.
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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 | 0.001 | 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 | 0.000 |
| 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 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".