Dermal wipe sampling method development and validation for semivolatile and nonvolatile flame-retardant compounds TBBPA and TPP for use in occupational exposure assessments
Bibliographic record
Abstract
Accurately estimating exposure is critical to assessing the potential health risks of chemicals. Characterizing dermal exposures to semivolatile or nonvolatile compounds in occupational studies can be challenging because of a lack of standardized procedures for dermal wipe sample collection and methods for sample analysis for most industrial chemicals, especially organic compounds. Methodologies are sometimes available in the scientific literature; however, the approaches vary, typically have not been validated, and may not be suitable for application in commercial laboratory settings. This article describes the laboratory development and validation of a method to identify and quantify the semivolatile organic compounds, tetrabromobisphenol A (TBBPA, CAS: 79-94-7) and triphenyl phosphate (TPP, CAS: 115-86-6) in dermal wipe samples and to validate recovery of these chemicals from porcine skin. The analytical method involved extraction of the test compounds on two different wipe media (cotton and polyester-rayon blend) in 100% isopropanol using gas chromatography-mass spectrometry. The results indicate that polyester-rayon wipes were preferable to cotton wipes. Additionally, the dermal wipe sampling method was tested and validated using porcine skin as a surrogate for human skin. This study provides a framework to perform validation of analytical and dermal sample collection methods for other semivolatile and nonvolatile chemicals and provides a baseline method for the development of commercial laboratory methods to evaluate exposure to other chemicals.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".