Exposure Assessment in a Single-Walled Carbon Nanotube Primary Manufacturer
Bibliographic record
Abstract
Objectives: This study was aimed at documenting and characterizing occupational exposure to single-walled carbon nanotubes (SWCNTs) generated in a primary manufacturing plant. It also compared various strategies of exposure monitoring. Methods: A 6-day measurement protocol was scheduled (D1-D6) including both (i) quasi-personal monitoring with an array of direct reading instruments (DRIs) and (ii) offline electron microscopy analyses of surface and breathing zone filter-based samples. The first step (D1 and D2) consisted of contamination screenings resulting from the various SWCNT production tasks using a multimetric approach. Surface sampling was also carried out to assess workplace cross-contamination. The second step (D3-D6) focused on the exposure monitoring during recovery/cleaning task, by comparing three personal elemental carbon (EC) measurements [respirable EC using a cyclone following the NIOSH 5040 method (REC-CYC), respirable and thoracic EC using parallel particle impactors [REC-PPI and TEC-PPI, respectively)] and gravimetric mass concentration measurements. Results: DustTrak DRX and electrical low-pressure impactor measurements indicated that particles were released during weighing, transferring, and recovery/cleaning tasks of the manufacturing process. Electron microscopy revealed the presence of agglomerated SWCNTs only during the recovery/cleaning task. REC-CYC concentrations remained under the limits of quantification; REC-PPI showed levels up to 58 µg m-3; and TEC-PPI ranged from 40 to 70 µg m-3. Ratios calculated between gravimetric measurements and estimated DustTrak mass concentrations ranged from 2.8 to 4.9. Cross-contamination appeared to be limited since SWCNTs was only found on surface samples collected close to the reactor in the production room. Conclusions: This case study showed that the DustTrak DRX should be the preferred device among DRIs to identify potential exposure to SWCNTs. However, there is a risk of false positive since it is a non-specific instrument; therefore, the actual release of SWCNTs must be confirmed with scanning electron microscopy/transmission electron microscopy analyses. Besides, using EC measurements as a proxy for SWCNT exposure assessments, as suggested by the NIOSH, is still challenging since interferences can occur with other EC sources such as carbon black, which is also present in the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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".