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Record W2966853104 · doi:10.1088/1361-6528/ab3697

Anomalous temperature dependent thermal conductivity of two-dimensional silicon carbide

2019· article· en· W2966853104 on OpenAlexaff
A. S. M. Jannatul Islam, Md. Sherajul Islam, Naim Ferdous, Jeongwon Park, Ashraful G. Bhuiyan, Akihiro Hashimoto

Bibliographic record

VenueNanotechnology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThermal conductivityMaterials sciencePhononCondensed matter physicsSilicon carbideThermal conductionCarbideComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Recently, two-dimensional silicon carbide (2D-SiC) has attracted considerable interest due to its exotic electronic and optical properties. Here, we explore the thermal properties of 2D-SiC using reverse non-equilibrium molecular dynamics simulation. At room temperature, a thermal conductivity of ∼313 W mK −1 is obtained for 2D-SiC which is one order higher than that of silicene. Above room temperature, the thermal conductivity deviates the normal 1/ T law and shows an anomalous slowly decreasing behavior. To elucidate the variation of thermal conductivity, the phonon modes at different length and temperature are quantified using Fourier transform of the velocity auto-correlation of atoms. The calculated phonon density of states at high temperature shows a shrinking and softening of the peaks, which induces the anomaly in the thermal conductivity. On the other hand, quantum corrections are applied to avoid the freezing effects of phonon modes on the thermal conductivity at low temperature. In addition, the effect of potential on the thermal conductivity calculation is also studied by employing original and optimized Tersoff potentials. These findings provide a means for better understating as well as designing the efficient thermal management of 2D-SiC based electronics and optoelectronics in near future.

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 score0.006
Threshold uncertainty score0.585

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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

Citations65
Published2019
Admission routes1
Has abstractyes

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