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Record W1982515393 · doi:10.1109/jsen.2014.2298853

Implications of a Low Stiffness Substrate in Flexural Plate Wave Sensing Applications

2014· article· en· W1982515393 on OpenAlexaff
Christoph Sielmann, Boris Stoeber, Konrad Walus

Bibliographic record

VenueIEEE Sensors Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceSubstrate (aquarium)StiffnessFinite element methodComposite materialFlexural strengthStress (linguistics)Structural engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Kirchhoff plate equations are examined for flexural plate wave sensors with a polymeric substrate that is mechanically similar to the polymer sensing layers commonly used in mass loading and gas sensing applications. The new analytical derivations for sensitivity are examined both experimentally and through finite element analysis (FEA). For substrates with a large in-plane stress, the behavior of the sensor is consistent with other gravimetric sensors based on flexural plate waves. In the case of low stress, the influence of variations in substrate stiffness and stress significantly outweigh the effects of mass loading in sensor frequency response. Experimental results using a poly(vinyl alcohol) sensing layer and poly(vinylidene fluoride) substrate are compared with analytical and FEA models, and demonstrate good adherence. The FEA models are also used to illustrate the relevant influences of mass loading, variations in substrate stiffness, and stress on the sensor response.

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)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.242
Teacher spread0.225 · 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
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

Citations1
Published2014
Admission routes1
Has abstractyes

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