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Record W2429477146 · doi:10.1088/0965-0393/24/5/055015

Atomistic simulations of material damping in amorphous silicon nanoresonators

2016· article· en· W2429477146 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsMaterials scienceAmorphous solidSiliconAmorphous siliconMolecular dynamicsCondensed matter physicsEngineering physicsComposite materialCrystallographyCrystalline siliconOptoelectronicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Atomistic simulations using molecular dynamics (MD) are emerging as a valuable tool for exploring dissipation and material damping in nanomechanical resonators. In this study, we used isothermal MD to simulate the dynamics of the longitudinal-mode oscillations of an amorphous silicon nanoresonator as a function of frequency (2 GHz–50 GHz) and temperature (15 K–300 K). Damping was characterized by computing the loss tangent with an estimated uncertainty of 7%. The dissipation spectrum displays a sharp peak at 50 K and a broad peak at around 160 K. Damping is a weak function of frequency at room temperature, and the loss tangent has a remarkably high value of ~0.01. In contrast, at low temperatures (15 K), the loss tangent increases monotonically from 4 × 10 − 4 to 4 × 10 − 3 as the frequency increases from 2 GHz to 50 GHz. The mechanisms of dissipation are discussed.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.235

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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