MétaCan
Menu
Back to cohort
Record W1976830598 · doi:10.1109/icsens.2013.6688512

Dielectric powder characterization by radio frequency measurements technique for hydrogen sensor applications: Application to iron oxide

2013· article· en· W1976830598 on OpenAlexafffund
N. Boubekeur, Hatem El Matbouly, Frédéric Domingue

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDielectricMaterials sciencePermittivityCharacterization (materials science)HydrogenRadio frequencyRelative permittivityAnalytical Chemistry (journal)OptoelectronicsComputer scienceChemistryTelecommunicationsNanotechnology

Abstract

fetched live from OpenAlex

This work presents a characterization technique developed to study the gas sensitivity of dielectric powder in Radio Frequency (RF) domain for hydrogen sensing application at room temperature. The method is based on the measurement of S parameters of sample holder loaded with dielectric powder. The sample holder used is designed and adapted to fit in a gas test bench for materials characterization under gas environment. The non-ideal cylindrical sample holder is modeled to extract dielectric permittivity of the filled powder in the frequency range of 25 MHz to 350 MHz. Then, the measurement technique with the designed setup is applied to iron (II, III) oxide dielectric powder to extract dielectric permittivity variation under hydrogen atmosphere. The results show a significant dielectric constant variation in presence of hydrogen. In addition, the presented model has advantages of a high accuracy in wide permittivity range validity from 1 to 25. This technique allows characterizing dielectric material for sensing application and to be used for other gas sensing application.

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: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.799

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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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
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

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

Same topicAcoustic Wave Resonator TechnologiesFrench-language works237,207