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Record W6958099154 · doi:10.60692/deprt-ftg24

Exceptional in-plane and interfacial thermal transport in graphene/2D-SiC van der Waals heterostructures

2020· article· en· W6958099154 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGrapheneThermal conductionThermal conductivityvan der Waals forceHeterojunctionThermalRectificationThermal resistance

Abstract

fetched live from OpenAlex

Abstract Graphene based van der Waals heterostructures (vdWHs) have gained substantial interest recently due to their unique electrical and optical characteristics as well as unprecedented opportunities to explore new physics and revolutionary design of nanodevices. However, the heat conduction performance of these vdWHs holds a crucial role in deciding their functional efficiency. In-plane and out-of-plane thermal conduction phenomena in graphene/2D-SiC vdWHs were studied using reverse non-equilibrium molecular dynamics simulations and the transient pump-probe technique, respectively. At room temperature, we determined an in-plane thermal conductivity of ~ 1452 W/m-K for an infinite length graphene/2D-SiC vdWH, which is superior to any graphene based vdWHs reported yet. The out-of-plane thermal resistance of graphene → 2D-SiC and 2D-SiC → graphene was estimated to be 2.71 × 10 −7 km 2 /W and 2.65 × 10 −7 km 2 /W, respectively, implying the absence of the thermal rectification effect in the heterobilayer. The phonon-mediated both in-plane and out-of-plane heat transfer is clarified for this prospective heterobilayer. This study furthermore explored the impact of various interatomic potentials on the thermal conductivity of the heterobilayer. These findings are useful in explaining the heat conduction at the interfaces in graphene/2D-SiC vdWH and may provide a guideline for efficient design and regulation of their thermal characteristics.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.798

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.201
Teacher spread0.172 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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