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Record W4401314815 · doi:10.1016/j.chroma.2024.465219

PFAS emissions from functional textiles using micro-chamber and thermal desorption coupled to two-dimensional gas chromatography-time of flight mass spectrometry (TD-GC×GC-TOF MS)

2024· article· en· W4401314815 on OpenAlexaff
Rocio Aranda‐Rodriguez, Ariadne Piperakis, Jonathan J. Grandy, Laura McGregor, Nadin Boegelsack, Hannah Calder, Matthew Edwards, William Papas, Jasper Che, S J Shields

Bibliographic record

VenueJournal of Chromatography A · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsHealth Canada
Fundersnot available
KeywordsChemistryMass spectrometryChromatographyThermal desorptionGas chromatography–mass spectrometryGas chromatographyTime-of-flight mass spectrometryVolatile organic compoundDesorptionAnalytical Chemistry (journal)AdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Per- and polyfluoroalkyl substances (PFAS), in the polymeric form, have been used extensively in functional textiles, including firefighter's turnout gear (e.g., jackets and pants), where PFAS are applied to confer oil and water resistance. However, growing concerns over the persistence, potential toxicity, and environmental impact of PFAS have prompted a thorough assessment of potential exposure pathways. Here, we report the emission of PFAS from three firefighter turnout gear jackets at 38 °C. Volatile emissions from the three layers (outer layer, moisture barrier, and thermal liner) were collected onto sorbent tubes via dynamic headspace sampling using a micro-scale chamber device kept at 38 °C for one hour. The emission was characterized using thermal desorption (TD) coupled to two-dimensional gas chromatography - time-of-flight mass spectrometry (GC×GC–TOF MS). The enhanced separation capacity of GC×GC was essential due to the high number of compounds present in each sample, especially for the fabrics from used turnout gear jackets. Based on the filtering expressions, including two-dimensional retention time (1tr and 2tr) and PFAS diagnostic fragment ions (m/z 69, 95, and 131), fluorotelomer alcohols (FTOHs) and fluorotelomer acrylates were identified using standards and spectral matching with the NIST database. After conducting a non-targeted tile-based workflow, jackets (both used and unused) and layers were compared, resulting in the identification of the top 15 discriminating features from over 400 chromatographic peaks. Finally, preliminary FTOH emission experiments showed some usage and layers trends that are aligned with those reported using solvent extraction. Highest levels of FTOHs were found in the moisture barrier, followed by the outer layer and the thermal liner. Older jackets emitted higher levels of 8:2 FTOH and 10:2 FTOH than a newer jacket. In contrast, a newer jacket used for one year had emissions containing higher levels of 6:2 FTOH. Investigating routes of exposure and identifying new PFAS targets are critical steps in evaluating the environmental and health impacts of these persistent chemicals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.256
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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