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Mechanical spectroscopy of nanocrystalline aluminum films: effects of frequency and grain size on internal friction

2012· article· en· W2170798172 on OpenAlexafffund
Guruprasad Sosale, Dorothée Almecija, Kaushik Das, Srikar Vengallatore

Bibliographic record

VenueNanotechnology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsMaterials scienceNanocrystalline materialGrain sizeInternal frictionAnnealing (glass)Grain boundaryDissipationComposite materialGrain growthMetallurgyNanotechnologyMicrostructureThermodynamics

Abstract

fetched live from OpenAlex

Energy dissipation by internal friction is a property of fundamental interest for probing the effects of scale on mechanical behavior in nanocrystalline metallic films and for guiding the use of these materials in the design of high-Q micro/nanomechanical resonators. This paper describes an experimental study to measure the effects of frequency, annealing and grain size on internal friction at room temperature in sputter-deposited nanocrystalline aluminum films with thicknesses ranging from 60 to 120 nm. Internal friction was measured using a single-crystal silicon microcantilever platform that calibrates dissipation against the fundamental limits of thermoelastic damping. Internal friction was a weak function of frequency, reducing only by a factor of two over three decades of frequency (70 Hz to 44 kHz). Annealing led to significant grain growth and the average grain size of 100 nm thick films increased from 90 to 390 nm after annealing for 1 h at 450 (∘)C. This increase in grain size was accompanied by a decrease in internal friction from 0.05 to 0.02. Taken together, these results suggest that grain-boundary sliding, characterized by a spectrum of relaxation times, contributes to internal friction in these films.

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 score0.173
Threshold uncertainty score0.450

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 head0.004
GPT teacher head0.247
Teacher spread0.243 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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