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Record W1991569351 · doi:10.1021/ac901421s

Quenching-Resistant Multiple Micro-Flame Photometric Detector for Gas Chromatography

2009· article· en· W1991569351 on OpenAlexafffund
Taylor C. Hayward, Kevin B. Thurbide

Bibliographic record

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryGas chromatographyChromatographyQuenching (fluorescence)DetectorChromatography detectorGas chromatography ion detectorAnalytical Chemistry (journal)High-performance liquid chromatographyOpticsFluorescence

Abstract

fetched live from OpenAlex

A multiple flame photometric detector (mFPD) based on many flames operated in series is introduced for the detection of sulfur and phosphorus compounds. The method employs attributes of a previously developed micro counter-current flame technique to readily establish any number of very small compact flames inside a narrow quartz tube. Results show for the first time that a five flame mFPD mode can improve hydrocarbon quenching resistance nearly 20-fold relative to a single flame (i.e., conventional FPD) mode, and nearly 10-fold relative to a two flame (i.e., dual FPD) mode. Under these conditions, the five flame mFPD mode is shown to maintain about 60% of its original analyte chemiluminescence even in the presence of over 100 mL/min of methane flow into the detector. In contrast to a conventional dual FPD device, the five flame mFPD mode also provides analyte sensitivity that is similar to a conventional FPD. Of note, the mFPD yields minimum detectable limits for sulfur and phosphorus of 4 x 10(-11) g S/s and 3 x 10(-12) g P/s respectively. Analyte selectivity over hydrocarbons, signal reproducibility, and response equimolarity are also improved in the mFPD, making it a potentially useful detector for applications in gas chromatography.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.242
Teacher spread0.232 · 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

Citations17
Published2009
Admission routes2
Has abstractyes

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