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Record W4392772504 · doi:10.1002/tee.24041

Colorimetric Readout Based Photoionization Detector for Gas Chromatographs

2024· article· en· W4392772504 on OpenAlexfundno aff
Jingqin Mao, L. D. Liu, Yahya Atwa, Junming Hou, Hamza Shakeel

Bibliographic record

VenueIEEJ Transactions on Electrical and Electronic Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
FundersQueen's UniversityHigher Education AuthorityQueen's University Belfast
KeywordsPhotoionizationHelium ionization detectorDetectorAcetoneDetection limitSIGNAL (programming language)IonizationAnalytical Chemistry (journal)Gas chromatographyDischarge ionization detectorChemistryHeliumChromatographyMaterials scienceOpticsAtomic physicsPhysicsIonComputer science

Abstract

fetched live from OpenAlex

This paper presents a new scheme for readout of a photoionization detector (PID) that utilizes the ionization luminescence of target gases in helium plasma to generate an output signal. Our signal readout method is based on processing the recorded video and correlating the light peak intensity with the sample injection time and concentration. We successfully demonstrated the feasibility of a colorimetric readout‐based photoionization detector coupled with a gas chromatography column by detecting a mixture of non‐polar compounds (alkanes) and two polar compounds (acetone and ethanol). The detection limit for our first‐generation device was calculated to be ~118 ng for acetone and around 26 ng for ethanol. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.004
GPT teacher head0.187
Teacher spread0.184 · 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
GenreMethods

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

Citations2
Published2024
Admission routes1
Has abstractyes

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