MétaCan
Menu
Back to cohort
Record W2908194971 · doi:10.1063/1.5068681

Love waves dispersion by phononic pillars for nano-particle mass sensing

2019· article· en· W2908194971 on OpenAlexaff
Jérémy Bonhomme, Mourad Oudich, B. Djafari-Rouhani, F. Sarry, Y. Pennec, Bernard Bonello, D. Beyssen, Paul G. Charette

Bibliographic record

VenueApplied Physics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueCentre National d’Etudes SpatialesAgence Nationale de la Recherche
KeywordsMaterials scienceSurface waveDispersion relationEffective mass (spring–mass system)Love waveLamb wavesSurface acoustic waveCondensed matter physicsMechanical waveOpticsPhysicsWave propagationClassical mechanicsLongitudinal wave

Abstract

fetched live from OpenAlex

We present a design of a pillared phononic crystal based structure for Love wave manipulation to achieve high mass sensitivity. The structure is made of phononic micro-pillars constructed by stacking tungsten and SiO2 layers, distributed on a substrate designed for Love wave propagation. The multilayered pillar allows the creation of bandgaps, which leads to the existence of resonant modes where the elastic energy is confined within the SiO2 free surface layer of the pillar. We study particularly a resonant mode where this layer exhibits torsional mechanical motion which can only be excited by shear horizontal surface waves. We show that Love wave interaction with the torsional mode gives rise to a sharp attenuation in the surface wave transmission spectrum with a high quality factor. We also study the variation of the mass sensitivity of the system by evaluating the resonant mode's frequency shift induced by a mass perturbation using two theoretical approaches: a perturbation theory based approximation and a numerical method. The system presents very promising mass sensitivity which provides an interesting approach to increase the detection performance of Love wave based bio-sensors.

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: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.832

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)0.0000.001

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 head0.007
GPT teacher head0.205
Teacher spread0.197 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics LettersSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207