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Record W4381611938 · doi:10.1139/cjc-2023-0051

Characteristics of a novel alkali flame ionization detector as an air sensor for volatile organo-nitrogen compounds

2023· article· en· W4381611938 on OpenAlexafffundvenue
Cole M. Mogenson, Kevin B. Thurbide

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryNitrogenAnalyteDetection limitAnalytical Chemistry (journal)DetectorFlame ionization detectorSelectivityAlkali metalChromatographyGas chromatographyOrganic chemistryOptics

Abstract

fetched live from OpenAlex

A novel alkali flame ionization detector (AFID) is introduced and characterized for use as an air sensor (i.e., without chromatographic separation) for organo-nitrogen compounds. Using a planar channeled quartz design, a simple, lightweight architecture for the portable device is presented, which yields sensitive and selective response toward nitrogen-containing analytes. For instance, the AFID sensor offers a detection limit of 30 pg N/s and a selectivity for nitrogen response over carbon of nearly two orders of magnitude. Further, nitrogen response is linear over the 1000-fold range of concentrations investigated. Relative to a flame photometric detector (FPD) device also used in a sensor mode, the AFID is observed to provide 100 times greater S/N values for nitrogen analytes with a nitrogen-to-carbon selectivity that is about 15 times larger. When using the AFID and FPD sensors in tandem, it is found that the response ratio of the simultaneous signals generated by each produces characteristic values that more clearly identify the presence or absence of nitrogen in unknown analytes. The AFID sensor was used to detect organo-nitrogen analytes in several samples, and results indicate that it could be a useful approach for portable air sensing of nitrogen compounds.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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