MétaCan
Menu
Back to cohort
Record W2965992698 · doi:10.11159/htff19.152

Effect of Electric Field on Interfacial Thermal Resistance Between Silicon and Water at Nanoscales

2019· article· en· W2965992698 on OpenAlexvenueno aff
Onur Yenigun, Murat Barışık

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 Bilimler Akademisi
KeywordsSiliconMaterials scienceElectric fieldThermal resistanceThermalOptoelectronicsField (mathematics)Composite materialElectrical resistance and conductanceEngineering physicsEngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, heat transfer rate of a nano-confined liquid is controlled by applying an electric field parallel to the heat transfer direction. Molecular Dynamics simulations are performed for deionized water confined between silicon slabs, where their surfaces oppositely charged to create an electric field perpendicular to the silicon wall to promote the electrowetting. Electric field strengths used in this study are 0, 0.18 and 0.35 V/nm. To investigate the effect of electric field on heat transfer, first water density profiles near the silicon walls are examined. Results shows that by applying electric field, water molecules near the silicon walls develop layering, which indicates the increased solid/liquid coupling. With the increasing electric field strength, an increase in the peak of the density layering is observed. Furthermore, heat transfer at the solid/liquid interface is characterized with the Kapitza length values. The results show that applying electric field reduces the interfacial thermal resistance between water and silicon due to the increased solid/liquid coupling and doubles the total heat flux.

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 score0.001
Threshold uncertainty score0.692

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 science0.0000.000
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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