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Record W2163764596 · doi:10.1093/chromsci/45.10.664

Capillary Flow Technology with Multi-Dimensional Gas Chromatography for Trace Analysis of Oxygenated Compounds in Complex Hydrocarbon Matrices

2007· article· en· W2163764596 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Chromatographic Science · 2007
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsChemistryChromatographyHydrocarbonCapillary actionDetection limitAnalytical Chemistry (journal)Gas chromatographyMethanolCorrelation coefficientPropanolVolumetric flow rateOrganic chemistry

Abstract

fetched live from OpenAlex

By employing multi-dimension gas chromatography with capillary flow technology in combination with highly selective capillary columns and a pressurized liquid injection system, light oxygenated compounds such as methanol, ethanol, n-propanol, 2-propanol, and n-butanol in the presence of either light hydrocarbon, heavy hydrocarbon, or aromatic matrices can be measured accurately with minimal possibility of a false positive. Using this technique, a detection limit of at least 0.20 ppm (w/w) with a linear correlation coefficient greater than 0.9993 over a range from 0.5 ppm to 600 ppm (w/w) and a relative standard deviation of greater than 2.7% are achieved for the solutes tested. The technique can also be effective for the measurement of other classes of oxygenated compounds such as ethers, aldehydes, and ketones. Another added benefit for the implementation of capillary flow technology is the capability to conduct column back-flushing, where heavier, undesired solutes in a sample can be back-flushed from the chromatographic system to improve system cleanliness and sample 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.

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)0.002
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 score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.018
Science and technology studies0.0000.002
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 head0.013
GPT teacher head0.267
Teacher spread0.253 · 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