Physical and Chemical Characterization of Beryllium Particles from Several Workplaces in Québec, Canada—Part A: Determining Methods for the Analysis of Low Levels of Beryllium
Bibliographic record
Abstract
Chemical and physical characterizations of beryllium (Be) particles found in settled dust samples from four industries based in Québec were attempted using a variety of analytical methods. Bulk particle chemistry was determined using inductively coupled plasma-mass spectrometry (ICP-MS), graphite furnace atomic absorption spectrometry (GFAAS), and instrumental neutron activation analysis (INAA). Time-of-flight secondary-ion mass spectrometry (TOF-SIMS), transmission electron microscopy, scanning electron microscopy, energy-dispersive spectroscopy, x-ray diffraction (XRD), electron energy loss spectrometry (EELS), and Auger microscopy were used to characterize physicochemical properties of particles. These analyses were deemed important based on the hypotheses that (1) different chemical forms of Be do not present the same risks, and (2) different morphologies lead to different risks. Standards were used to prove the adequacy of XRD, EELS, and Auger microscopy prior to the analyses of industrial samples. However, low concentrations of Be in samples were a limiting factor for most methods; few detected Be in industrial samples. Only ICP-MS, GFAAS, and TOF-SIMS were able to detect Be in industrial samples analyzed in this study. Characterization of settled dust samples showed high number of Be particles, even for Be concentrations below 100 ppm. Furthermore, Be seems to be present as fine particles of Be metal, possibly mechanically agglomerated or aggregated to larger particles or compounds such as cryolite. Other major elements detected with INAA present in the samples were limited to Na, Al, Ca, and F. It was concluded that TOF-SIMS is a valid method for characterizing particles containing approximately 0.01% Be.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".