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Record W2317222295 · doi:10.1115/imece2015-50675

Predicting Phonon Thermal Transport in Two-Dimensional Graphene-Boron Nitride Superlattices at the Short-Period Limit

2015· article· en· W2317222295 on OpenAlexafffund
Carlos B. da Silva, Fernán Saiz, David A. Romero, Cristina H. Amon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSuperlatticePhononCondensed matter physicsMaterials scienceZigzagGrapheneThermal conductivityBoron nitrideBoltzmann equationPhysicsNanotechnologyThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Two-dimensional superlattices are promising alternatives to traditional semiconductors for manufacturing power-dissipating devices with enhanced thermal and electronic properties. The goal of this work is to investigate the influence of the superlattice secondary periodicity and atomic interface orientation on the phonon properties and thermal conductivity of two-dimensional superlattices of graphene and boron nitride. We have employed harmonic lattice dynamics to predict the phonon group velocities and specific heats, and molecular dynamics to extract the relaxation times from normal mode analysis in the frequency domain. Density functional perturbation theory is applied to validate the phonon dispersion curves. The Boltzmann transport equation under single relaxation time approximation is then used to predict the thermal conductivities of the superlattices in the zigzag and armchair orientations with periodicities between one and five. Our results showed that the thermal conductivities increased by 15.68% when reducing the superlattice period from two to one. In addition, thermal conductivities parallel to the interface increase by 20.15% when switching the orientation from armchair to zigzag.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.263
Teacher spread0.224 · 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.

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

Citations2
Published2015
Admission routes2
Has abstractyes

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