Characterization of Beryllium Particles from CAlSiFrit
Bibliographic record
Abstract
Aluminum smelters produce in excess thousand of tons of spent pot lining (SPL) each year. CAlSiFrit technology is a recycling process in which spent pot lining (SPL) is recovered and transformed into commercial value-added products. Since SPL contains beryllium (Be), exposures encountered by workers may result in adverse effects. This study aimed to establish the level at which Be is present in the CAlSiFrit and to determine the chemical and physical characteristics of the Be-containing particles. Three samples of CAlSiFrit powder supplied by the recycling industry were analyzed using several methods in order to (1) detect and characterize Be-containing particles, (2) identify the Be chemical form, and (3) quantify the amount of other major chemical elements present. These methods were: inductively coupled plasma-mass spectrometry, instrumental neutron activation analysis, time-of-flight secondary-ion mass spectrometry (TOF-SIMS), analytical transmission electron microscopy (ATEM), and x-ray diffraction. Results show that the three samples have a similar chemical composition, with high concentrations, of Si, Ca, Al, Na, F, Fe, K, Mg, and Ti, in decreasing order. Be concentrations were low and totaled less than 3 ppm. The size of the areas where Be was detected by TOF-SIMS is approximately 0.3 mum or less in diameter. A large quantity of oxygen in the particles of dusts was observed. As the majority of elements present have a great affinity for oxygen, the presence of oxygen indicates that these elements are probably oxides. Finally, the particle size varied from approximately 0.05 to 1 mum. This is consistent with the interpretation of the TOF-SIMS results that suggest a size of approximately 0.3 mum or less for the particles containing Be. These results are important from the perspective that thousands of tons of CAlSiFrit, a supplementary cement material, might be produced and used.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".