Metal impurities provide useful tracers for identifying exposures to airborne single-wall carbon nanotubes released from work-related processes
Bibliographic record
Abstract
This study investigated the use of metal impurities in single-wall carbon nanotubes (SWCNT) as potential tracers to distinguish engineered nanomaterials from background aerosols. TEM and SEM were used to characterize parent material and aerosolized agglomerates collected on PTFE filters using a cascade impactor. SEM image analysis indicated that the SWCNT agglomerates contained about 45% amorphous carbon and backscatter electron analysis indicated that metal impurities were concentrated within the amorphous carbon component. Two elements present as impurities (Y and Ni) were selected as appropriate tracers in this case as their concentrations were found to be highly elevated in the SWCNT parent material (% range) compared to ambient air particles (μg/g range), and background air concentrations were below detection limits for both elements. Bioaccessibility was also determined using physiologically-based extractions at pH conditions relevant to both ingestion and inhalation pathways. A portable wet electrostatic precipitation system effectively captured airborne Y and Ni released during sieving processes, in proportions similar to the bulk sample. These observations support the potential for catalysts and other metal impurities in carbon nanotubes to serve as tracers that uniquely identify emissions at source, after an initial analysis to select appropriate tracers.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".