Determination of Metal Impurities in Carbon Nanotubes Sampled Using Surface Wipes
Bibliographic record
Abstract
Residual metal impurities in carbon nanotubes (CNTs) provide a means to distinguish CNT from non-CNT sources of elemental carbon in environmental samples. A practical and cost-effective analytical approach is needed to support routine surface monitoring of CNT metal tracers using wipe sampling. Wipe sampling for CNT metal tracers is considered a qualitative indicator of the presence of CNTs, not a quantitative exposure metric. In this study, two digestion approaches (microwave-assisted nitric acid/H 2 O 2 digestion and ultrasonic nitric/HF acid digestion) in conjunction with Inductively Coupled Plasma Mass Spectrometry (ICP-MS) determination were evaluated for their ability to extract metal impurities from CNT particles captured on wipe substrates. Aliquots of different carbon nanotubes (including NIST 2483 single-wall CNT) with and without GhostWipes™ (ASTM E-1792 compliant) were used to compare the performance of the digestion methods. The microwave digestion method accommodated the bulky wipe sample and also eliminated potential ICP-MS signal interferences related to incomplete digestion. Although quantitative recoveries requiring lengthy multistep digestion protocols may be necessary in other applications, the near-total recoveries achieved in the present study for CNT catalyst elements were adequate for identifying surface contamination of CNTs in the workplace using wipe sampling.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".