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Record W2002536047 · doi:10.1021/ac701871q

Kinetic Calibration Using Dominant Pre-equilibrium Desorption for On-Site and in Vivo Sampling by Solid-Phase Microextraction

2007· article· en· W2002536047 on OpenAlexaff
Simon Ningsun Zhou, Wennan Zhao, Janusz Pawliszyn

Bibliographic record

VenueAnalytical Chemistry · 2007
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryDesorptionCalibrationAnalytical Chemistry (journal)Solid-phase microextractionMatrix (chemical analysis)Calibration curveAbsorption (acoustics)Phase (matter)ChromatographyDetection limitAdsorptionMass spectrometryMaterials sciencePhysical chemistryOrganic chemistryGas chromatography–mass spectrometry

Abstract

fetched live from OpenAlex

A new kinetic calibration was developed using dominant pre-equilibrium desorption by solid-phase microextracton. The calibration was based on isotropism between absorption and desorption, which was proved theoretically and experimentally in an aqueous solution and semisolid matrix. This approach allows for the calibration of absorption using desorption to compensate for matrix effects. Moreover, concentration profiles are initially proposed to verify isotropism between the absorption and desorption, while providing a linear approach to obtain time constants for the purpose of quantitative analysis. This linear approach is more convenient, robust, and accurate than the nonlinear version with the previously used time profiles. Furthermore, the target analytes are used as the internal standards; thus, radioactive or deuterated internal standards are not necessary. In addition, dominant pre-equilibrium desorption utilizes the pre-equilibrium approach and offers a shorter sample preparation time, which is typically suitable for in vivo sampling. This kinetic calibration method was successfully applied to prepare samples of polycyclic aromatic hydrocarbons in a flow-through system and in vivo pesticide sampling in a jade plant (Crassula ovata).

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)
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 score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.380
Teacher spread0.341 · 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

Citations57
Published2007
Admission routes1
Has abstractyes

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