MétaCan
Menu
Back to cohort
Record W3115584235 · doi:10.1139/cjp-2020-0328

Theoretical study of thermal response of bimaterial microcantilevers with different coating materials

2020· article· en· W3115584235 on OpenAlexvenueno aff
Le Tri Dat, Vinh N. T. Pham, Ho Thanh Huy, Takuya Iida, Nguyen Duy Vy

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsnot available
Fundersnot available
KeywordsCantileverDeflection (physics)ThermalThermal conductivityCoatingExcitationPhysicsThermal effusivityComposite materialMaterials scienceThermal resistanceOpticsThermal contact conductanceThermodynamics

Abstract

fetched live from OpenAlex

Bilayer microcantilevers are a versatile tool in thermal and bio-sensing with responses relying on the mismatch between the two constituting materials. The cantilever response, such as a deflection and resonance frequency shift, could be involved when the cantilever is in contact with an arbitrary heat source in the ambient environment. In this study, thermally induced deflection will be theoretically examined assuming a heat source located at various positions on the cantilever. The combined contributions of heat absorption, thermal conductivity, and material rigidity on the final deflection will be revealed. Selecting an optimal position leads to 1.5 times enhancement of the cantilever deflection in comparison to thermal excitation at the cantilever end in conventional experiments, which implies a significant increase in thermal sensitivity. Furthermore, responses of cantilevers with different coating materials (Au, Al, Cu, or Ni) have been examined and show a dominant sensitivity of Al- and Ni- over Cu- and Au-coated cantilevers. These results could help to explain recent experimental results and to choose an optimal thermal excitation of microcantilevers in sensing.

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 score0.183
Threshold uncertainty score0.814

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)0.0010.000

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 head0.012
GPT teacher head0.215
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
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicMechanical and Optical ResonatorsFrench-language works237,207