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Record W2117328675 · doi:10.1093/chromsci/44.4.219

Stacked Injection with Low Thermal Mass Gas Chromatography for PPB Level Detection of Oxygenated Compounds in Hydrocarbons

2006· article· en· W2117328675 on OpenAlexaff
Jim Luong, R. Gras, Hernan Cortes, Robert V. Mustacich

Bibliographic record

VenueJournal of Chromatographic Science · 2006
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsDow Chemical (Canada)
FundersDow Chemical Company
KeywordsChemistryChromatographyGas chromatographyHydrocarbonOrganic chemistry

Abstract

fetched live from OpenAlex

The presence of oxygenated compounds in light hydrocarbons can have a negative impact in manufacturing processes and on the quality of products produced. The development of an analytical technique termed "stacked injection" has been reported earlier. With this technique, sensitivity in the parts-per-billion (ppb) range for oxygenated compounds can be achieved, even with a flame ionization detector; however, there are drawbacks for this approach that limit its overall effectiveness. A new, improved analytical technique has been developed that not only addresses the shortcomings encountered, but offers markedly higher analytical performance. The new concept employs multidimensional gas chromatography (GC) with low thermal mass GC. With this new approach, throughput improvements of up to 5 times, range extension of solutes amenable for this analysis of up to nC16 alcohol, and ppb levels of detection for oxygenated compounds are achieved. Apart from alcohols, this technique is successfully employed for the ppb level analysis of other classes of oxygenated compounds, such as ethers, aldehydes, and aromatics.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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