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Record W2052468282 · doi:10.1002/rcm.2551

A coaxially heated membrane introduction mass spectrometry interface for the rapid and sensitive on‐line measurement of volatile and semi‐volatile organic contaminants in air and water at parts‐per‐trillion levels

2006· article· en· W2052468282 on OpenAlexafffund
Alex J. Thompson, A. Skye Creba, Robyn M. Ferguson, Erik T. Krogh, Christopher G. Gill

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsVancouver Island University
FundersNatural Sciences and Engineering Research Council of CanadaBritish Columbia Knowledge Development FundUniversities Space Research AssociationUniversity of Washington
KeywordsChemistryPolydimethylsiloxaneMass spectrometryAnalytical Chemistry (journal)ChromatographyAnalyteParts-per notationVolatile organic compoundMembrane

Abstract

fetched live from OpenAlex

A coaxially heated membrane introduction mass spectrometry (MIMS) sampling interface is presented that demonstrates improved on-line performance for the direct measurement of semi-volatile organic compounds (SVOCs) in air and water samples at parts-per-trillion levels. The device is based on a polydimethylsiloxane (PDMS) capillary hollow fibre membrane (HFM) in a pneumatically assisted "flow-over" configuration that is resistively heated on the membrane interior via a coaxial nichrome wire, establishing a thermal gradient counter to the analyte concentration gradient. This arrangement allows for continuous and/or pulsed heating modes, affording excellent sensitivity for the on-line measurement of SVOCs while retaining sensitivity for volatile organic compounds (VOCs). In addition, the signal response time for SVOCs is reduced substantially over conventional "flow-over" MIMS interfaces. Separation and quantitation of analytes are achieved using quadrupole ion trap tandem mass spectrometry.

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 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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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

Citations51
Published2006
Admission routes2
Has abstractyes

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