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Record W2044751891 · doi:10.1002/rcm.3255

The combined use of thermal desorption and selected ion flow tube mass spectrometry for the quantification of xylene and toluene in air

2007· article· en· W2044751891 on OpenAlexaff
Brian Ross, Natasha Vermeulen

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNOSM UniversityLakehead University
Fundersnot available
KeywordsChemistryTolueneThermal desorptionMass spectrometryChromatographyXyleneSorbentDesorptionVolatile organic compoundAnalytical Chemistry (journal)Gas chromatographyAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Thermal desorption (TD) is commonly employed for volatile chemical analysis, it being the method of choice for occupational health and safety monitoring. TD allows for offline capture of volatiles onto a solid sorbent followed by desorption and analysis at a later time. Although TD is routinely used in conjunction with gas chromatography (TD-GC), the assay throughput is low and requires the use of gas standards for quantification. Another technique increasingly employed for volatile chemical analysis, selected ion flow tube mass spectrometry (SIFT-MS), is capable of real-time absolute (i.e. without calibration standards) quantification of volatile chemicals present at single digit parts per billion or higher concentrations. SIFT-MS is, however, normally used for online direct analysis of gas samples rather than offline collection and analysis. The goal of this study was to determine whether a combination of TD and SIFT-MS could be used to quantify volatile compounds, specifically xylene and toluene, more rapidly than TD-GC and without the need for calibration standards. SIFT-MS was able to quantify xylene and toluene levels within 45 s of desorption. Due to the robustness of the SIFT-MS analysis in the presence of water vapour and other major components of air, the purging of tubes usually required to remove these constituents during the TD cycle was not required, therefore reducing the TD cycle time. Comparing the quantity of xylene and toluene applied to the TD tube with the absolute levels quantified by SIFT-MS subsequent to desorption suggested a recovery of over 95% of the applied compound. We conclude that the combination of TD and SIFT-MS allows more rapid and accurate quantification of xylene and toluene (compared with TD-GC) to be achieved without the need for calibration standards, features which may be advantageous in applications requiring rapid analysis and high throughput.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.258
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2007
Admission routes1
Has abstractyes

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