Solvent Removal of Beryllium from Surfaces of Equipment Made of Beryllium Copper
Bibliographic record
Abstract
Exposure to beryllium compounds, both by inhalation and skin contact, may result in immune sensitization and chronic beryllium disease. The objective of the present research work was to study the feasibility of removing beryllium compounds from the surfaces of devices made of Be-Cu alloy and to estimate the frequency at which the surfaces had to be rubbed in order to evaluate the likelihood that beryllium can be removed from the surfaces by serial wipe sampling at concentrations exceeding the US Department of Energy (DOE) standard limit of 0.2 microg per 100 cm2. The standard limit was exceeded after successive cleanings of moulds and plates made of Be-Cu alloy with solvents such Citranox, an acidic solvent, Alconox, Z-99 and Fantastik, basic solvents, or more neutral solvents such as Luminox and water. Citranox was the best solvent for extracting beryllium from the tested surfaces, while Alconox seemed to be the second best one. In general, warm water, Luminox and Z-99 seemed to be less efficient for extracting Be from all equipment. The results of the present study suggest that Ghost Wipes, when passed across a surface under the firm pressure of an individual's hand, can be used to detect beryllium contamination. However, they seem to show low reliability for quantification. From a safety standpoint in occupational settings, workers should be offered skin protection and respiratory protection if they have to handle devices made of Be-Cu alloy.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".