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Record W2334356134 · doi:10.1021/je300581n

Investigation of Effects of the Cosolvent Methanol on the Apparent Solubility of a Suite of Chlorobenzenes Using Headspace Solid-Phase Microextraction (HS-SPME)

2012· article· en· W2334356134 on OpenAlexaff
Kerry N. McPhedran, Rajesh Seth, Ken G. Drouillard

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2012
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsChemistryChromatographySolid-phase microextractionSolubilityChlorobenzeneMethanolVapoursGas chromatography–mass spectrometryOrganic chemistryMass spectrometry

Abstract

fetched live from OpenAlex

Cosolvent effects have historically been considered as negligible at cosolvent spiking concentrations below 1 %. Headspace solid-phase microextraction (HS-SPME) was used to determine the effect of the cosolvent methanol (MeOH) on apparent solubility of three chlorobenzenes (CBs) (1,2,4,5-tetrachlorobenzene, pentachlorobenzene, and hexachlorobenzene). All CBs exhibited a ln-linear decreasing relationship between cosolvent volume fraction and extracted mass. However, there were no statistical differences in measured headspace CB concentration at cosolvent additions of 1 % or less compared to controls. This study validated the use of MeOH volumes of 1 % and lower for batch studies using HS-SPME. Ninety-six hour incubation studies using 0.01 %, 10 %, and 100 % MeOH treatments demonstrated chemical losses from headspace vials through time. Declines were greatest for 0.01 % MeOH treatments and nonsignificant for vials containing 100 % MeOH. The silanization of vials using 5 % DCMCS was unsuccessful in reducing chemical loss; therefore potential loss processes must be further investigated prior to using the HS-SPME method for incubated treatments.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.076
GPT teacher head0.350
Teacher spread0.275 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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