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Record W2033451641 · doi:10.1109/icsens.2012.6411188

Implications of a low stiffness substrate in lamb wave gas sensing applications

2012· article· en· W2033451641 on OpenAlexaff
Christoph Sielmann, Boris Stoeber, Konrad Walus

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceStiffnessSubstrate (aquarium)Finite element methodLayer (electronics)Gravimetric analysisComposite materialAcousticsStructural engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Numerical finite element analysis (FEA) of a poly(vinylidene fluoride) (PVDF) flexural plate wave (FPW) acoustic gravimetric gas sensor is used to study the performance implications of a soft polymeric substrate with a stiffness comparable to the stiffness of the gas sensing layer. The low substrate stiffness allows small gas-absorption induced variations in sensing layer stiffness to have a significant impact on the resonance frequency of the device, enabling greatly improved stiffness sensitivity compared with mass-only models derived for sensors with stiffer substrates. Experimental results from sensors with poly(vinyl alcohol) (PVA) as the affinity layer show good agreement with the model. Further simulations show that the choice of film thickness and substrate tensioning provide mechanisms for tuning the device performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.362

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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