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Record W1976219390 · doi:10.1021/ac010920f

Extending the Solid-Phase Microextraction Technique To High Analyte Concentrations:  Measurements and Thermodynamic Analysis

2002· article· en· W1976219390 on OpenAlexafffund
K.J. Gill, Wayne A. Brown

Bibliographic record

VenueAnalytical Chemistry · 2002
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsChemistrySolid-phase microextractionAnalyteChromatographyPhase (matter)Analytical Chemistry (journal)Mass spectrometryGas chromatography–mass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

The solid-phase microextraction (SPME) technique has been used historically to quantify analytes present at the parts per million level. However, the nonintrusive nature of SPME lends itself to other applications involving analytes at higher concentration. In the current work, the possibility of using the SPME technique to measure concentrated gaseous samples was examined. Pentane concentrations between 0 and 100% saturation were studied, over a temperature range of 20-45 degrees C. The results showed that, up to a critical mole fraction in the solid phase, the concentrations of pentane in the polymeric extracting solid and vapor phases were related by a constant, equal to Henry's constant. The temperature dependence of Henry's constant was shown to follow the predicted trend with temperature, as determined from rigorous thermodynamic calculations. Above the pentane concentration in the polymeric phase, the response deviated from linearity. The nonideality was captured in an activity coefficient. An activity coefficient model developed to describe the nonideality was found to be a function of the swollen volume of the SPME polymer phase. The results indicate that the SPME technique can be applied to high analyte concentrations, although difficulties may be encountered when multiple analytes are absorbed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.049
GPT teacher head0.353
Teacher spread0.304 · 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

Citations8
Published2002
Admission routes2
Has abstractyes

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