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Record W4379881870 · doi:10.6000/1929-5995.2023.12.04

Conjoint Effect of Boron Nitride and Surface-Enhanced Flake Graphite in Thermal Conductivity of Thermally Conductive Grease

2023· article· en· W4379881870 on OpenAlexvenueno aff
Xiaojun Xiong, Yifan Li

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceGreaseBoron nitrideThermal conductivityComposite materialGraphiteElectrical conductorThermal conductionInterfacial thermal resistanceThermal greaseThermalThermal resistanceThermodynamics

Abstract

fetched live from OpenAlex

The next generation of high-power electronic devices is expected to exhibit improved heat dissipation capabilities despite their smaller size. Current studies have investigated the utilization of hybrid fillers, our study introduces a novel approach by combining boron nitride (BN) and surface-enhanced flake graphite (G), both of which possess a platelet-like structure, to develop a thermally conductive grease. The grease shows an exceptionally high thermal conductivity of 2.21 W/mK and an extremely low electrical conductivity of 7.3×10-6 S/m. The viscosity of the grease is measured at 149 Pa·s. By incorporating hybrid fillers with a significantly high aspect ratio into EPON 828, a notable reduction of interfacial thermal resistance is observed, which is attributed to the formation of an effective pathway for phonon transfer facilitated by the unique characteristics of the hybrid fillers. Various theoretical models are employed to corroborate the experimental data, which facilitates substantiating the fundamental principles underlying the enhanced thermal conductivity of the prepared thermal grease.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.035
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.380
Teacher spread0.348 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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