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Record W2322806247 · doi:10.1149/1.3571979

An Electronic Nose for the Detection of Carbonyl Species

2011· article· en· W2322806247 on OpenAlexaff
Bhavana Deore, Gerardo A. Diaz‐Quijada, Danial D. M. Wayner, Duncan R. Stewart, Doyun Won, P. Waldron

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsElectronic noseConjugated systemPolymerLuminescenceFormaldehydeAnalyteDelocalized electronIndoor air qualityMaterials scienceChemical sensorNanotechnologyComputer scienceOptoelectronicsChemistryOrganic chemistryEnvironmental sciencePhysical chemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Conjugated polymers are an important class of materials due to their luminescent and electrical properties which result from electronic charge delocalization. Consequently, these polymers provide a suitable platform for new sensors since electronic and conformational changes that arise from the interaction with analytes translate into measurable changes in electrical conductivity or luminescence. The main advantage of this platform is based on the ability to partially tune the response of the sensor by changing the chemical structure of the polymer. The present work illustrates proof-of-concept chemical sensors that incorporate a molecular recognition site for the detection of common indoor air polluting carbonyl species such as aldehydes and ketones. Emphasis is placed on the detection of formaldehyde which poses major concerns in Indoor Air Quality (IAQ). By taking advantage of the differential response of various polymers towards analytes in question, sensor elements can be implemented in an array format to give an electronic nose

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: none
Teacher disagreement score0.829
Threshold uncertainty score0.202

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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