Introducing Eyewear Lenses as Passive Samplers for Assessing Inhalation Exposure to Airborne Chemicals
Bibliographic record
Abstract
Wearable passive samplers are inexpensive tools for assessing personal exposure to diverse contaminants through a combination of exposure pathways. This versatility, however, can be a disadvantage when it is critical to assess a specific pathway. Here, we aim to develop eyewear lenses as passive samplers that can capture inhalation exposure as a primary pathway for airborne contaminants. We designed polycarbonate lens samplers with and without a polydimethylsiloxane (PDMS) coating and compared their surface properties and chemical uptake. We compared the chemical profiles they sampled from indoor lab and office environments using solvent-soaked wipes to extract the chemicals by wiping the lens surfaces and analyzing the wipe extracts using non-targeted screening on LC-QTOF-MS. Compared to the comparably transparent uncoated lenses, the PDMS-coated lenses were up to six times rougher in morphology. This difference, unexpectedly, did not yield significant differences in the chemical profiles measured between the PDMS-coated and uncoated polycarbonate lenses. Combined, both lens types sampled over 900 features, with diverse physico-chemical properties, annotated at varying levels of confidence. Level 2 features include phthalates and organophosphate esters commonly used as plasticizers. These promising results form the foundation for further developing the lenses as passive sampling eyewear for easily assessing inhalation exposure at the population level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".