Detection of Carbon Nanotubes in Indoor Workplaces Using Elemental Impurities
Bibliographic record
Abstract
This study investigated three area sampling approaches for using metal impurities in carbon nanotubes (CNTs) to identify CNT releases in workplace environments: air concentrations (μg/m3), surface loadings (μg/cm2), and passive deposition rates (μg/m2/h). Correlations between metal impurities and CNTs were evaluated by collecting simultaneous colocated area samples for thermal-optical analysis (for CNTs) and ICP-MS analysis (for metals) in a CNT manufacturing facility. CNTs correlated strongly with Co (residual catalyst) and Ni (impurity) in floor surface loadings, and with Co in passive deposition samples. Interpretation of elemental ratios (Co/Fe) assisted in distinguishing among CNT and non-CNT sources of contamination. Stable isotopes of Pb impurities were useful for identifying aerosolized CNTs in the workplace environment of a downstream user, as CNTs from different manufacturers each had distinctive Pb isotope signatures. Pb isotopes were not useful for identifying CNT releases within a CNT manufacturing environment, however, because the CNT signature reflected the indoor background signature. CNT manufacturing companies and downstream users of CNTs will benefit from the availability of alternative and complementary strategies for identifying the presence/absence of CNTs in the workplace and for monitoring the effectiveness of control measures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".