MétaCan
Menu
Back to cohort
Record W2964719088 · doi:10.11159/htff19.169

Molecular Dynamics Study of the Thermal Conductivity of Graphene Coated Copper

2019· article· en· W2964719088 on OpenAlexvenueno aff
Kasim Toprak, Gizem Ersavaş

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsGrapheneCopperThermal conductivityMaterials scienceMolecular dynamicsConductivityDynamics (music)ThermalChemical physicsNanotechnologyCondensed matter physicsComposite materialChemistryMetallurgyPhysicsThermodynamicsComputational chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In this study, the thermal conductivity of various size of pure copper, pure graphene and, different number of layer graphene coated copper models are studied using non-equilibrium molecular dynamics (NEMD) simulations. Our findings show that the thermal conductivity of graphene coated copper is higher than the uncoated ones. Furthermore, results also indicate that single layer graphene (SLG) model has the highest thermal conductivity as compared to the other model. Even though multiple layer graphene (MLG) has lower thermal conductivity value compare to SLG, this study shows that the thermal conductivity of MLG coated copper has higher thermal conductivity than SLG coated one. The most important finding in this study suggests that the thermal conductivity of copper can be improved using high thermal conductivity materials like graphene.

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 score0.002
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.007
GPT teacher head0.196
Teacher spread0.190 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicThermal properties of materialsFrench-language works237,207