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Record W1988359763 · doi:10.1021/es001561t

Thin-Film Solid-Phase Extraction To Measure Fugacities of Organic Chemicals with Low Volatility in Biological Samples

2001· article· en· W1988359763 on OpenAlexafffund
John Wilcockson, Frank A. P. C. Gobas

Bibliographic record

VenueEnvironmental Science & Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFugacityChemistryBioaccumulationEnvironmental chemistryPartition coefficientVolatilisationOrganic chemicalsVolatility (finance)SolventPersistent organic pollutantBioconcentrationSolid phase extractionChromatographyExtraction (chemistry)HydrocarbonOrganic chemistry

Abstract

fetched live from OpenAlex

To investigate the environmental fate, food chain bioaccumulation, and toxicity of organic chemicals, it is often preferable to measure the chemical's fugacity rather than its concentration. However, simple methods to do this are rare. This paper presents a novel yet simple method to measure fugacities of a range of poorly volatile hydrophobic organic chemicals ranging in octanol-air partition coefficients from 10(5.6) to 10(9.2). Thin films of ethylene vinyl acetate coated on glass surfaces are used as solid-phase samplers of contaminated biological tissues. The technique is applied to fish tissue samples and spiked fish diets to determine method feasibility, equilibration times, reproducibility, and property characteristics of the thin films. It is concluded that the method provides an attractive technique to measure chemical fugacities in biological tissues without requiring solvent extractions and cleanup. The method is further expected to be applicable to investigate the fugacity of semivolatile and poorly volatile organic chemicals in air, water, sediments, and soil.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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.

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

Citations143
Published2001
Admission routes2
Has abstractyes

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