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Record W1516531837 · doi:10.1109/mwsym.2015.7166883

Polymeric sensing material-based selectivity-enhanced RF resonant cavity sensor for volatile organic compound (VOC) detection

2015· article· en· W1516531837 on OpenAlexaff
Weixu Chen, Katherine M. E. Stewart, Raafat R. Mansour, Alexander Penlidis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResonatorBenzeneAcetoneSensitivity (control systems)SelectivityMaterials scienceAnalyteNon-blocking I/OVolatile organic compoundAnalytical Chemistry (journal)OptoelectronicsChemistryOrganic chemistryElectronic engineeringChromatography

Abstract

fetched live from OpenAlex

This paper presents a novel approach to in-line chemical gas flow monitoring, employing a high-Q RF resonator coated with a polymeric sensing material. The polymeric sensing materials employed are OV-275 and P25DMA doped with 20% NiO. These materials are known to be responsive against various VCOs, and are individually coated on the post of a combline cavity resonator to help functionalize the sensor against specific analytes. The input of the resonator was deliberately designed to achieve the minimal loading to maximize the loaded Q of the resonator, thereby improving its sensitivity near its f0. The OV-275 sensor demonstrates sensitivity of 2.332 mdB/ppm and 0.348 mdB/ppm to acetone and benzene exposure, respectively. Similarly, the P25DMA sensor has a sensitivity of 0.199 mdB/ppm and 0.764 mdB/ppm to acetone and benzene, respectively.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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