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Record W4408663135 · doi:10.1021/acsestair.4c00281

Collecting Airborne Organochlorines on Polyurethane Foam: Comparison of Field Observations with a Breakthrough Model

2025· article· en· W4408663135 on OpenAlexafffundabout
Terry F. Bidleman, Fiona Wong, Helena Dryfhout-Clark, Hayley Hung, Mats Tysklind

Bibliographic record

VenueACS ES&T Air · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change Canada
FundersNorthern Contaminants ProgramCrown-Indigenous Relations and Northern Affairs CanadaSvenska Forskningsrådet FormasGovernment of Canada
KeywordsPolyurethaneField (mathematics)Environmental scienceMaterials scienceEnvironmental chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Polyurethane foam (PUF) is widely used for active air sampling (AAS) of gaseous semivolatile organic compounds (SVOCs). PUF efficiently collects SVOCs with moderate to low volatility, but applications are limited for the more volatile SVOCs due to breakthrough from the PUF trap. The collection efficiency can be predicted by frontal chromatography theory with knowledge of several parameters: the sampled air volume, the breakthrough volume which depends on the PUF/air partition ratio ( K PA ), and the number of theoretical plates ( N ) in the PUF trap. Here we evaluate data from two Canadian air monitoring programs in which front and back PUF traps (P1 and P2) were used to check for breakthrough, as indicated by the back/front ratio (P2/P1) of collected SVOCs. A frontal chromatography model was used to relate collection efficiency of hexachlorobenzene (HCB) and α-hexachlorocyclohexane (α-HCH) to their observed P2/P1 ratios under assumed scenarios of K PA and N, and apparent N -values for the PUF traps were derived. Results were applied to correcting observed air concentrations of HCB for breakthrough loss. The choice of K PA greatly influenced the adjusted air concentrations and their variation with temperature.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.290
Teacher spread0.260 · 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 teacher head, 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

Citations2
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueACS ES&T AirSame topicToxic Organic Pollutants ImpactFrench-language works237,207