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Record W2319970667 · doi:10.1021/ie3012775

Study on Liquid Crystal Polymer-Hexagonal Boron Nitride Composites for Hybrid Heat Sinks

2013· article· en· W2319970667 on OpenAlexaff
Siu N. Leung, Omer M. Khan, Hao Shi, Hani E. Naguib, F.P. Dawson, Vincent Adinkrah

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHexagonal boron nitrideMaterials scienceComposite materialBoron nitrideHexagonal crystal systemPolymerBoronCrystal (programming language)High heatHeat sinkCrystallographyChemistryNanotechnologyMechanical engineeringGrapheneOrganic chemistry

Abstract

fetched live from OpenAlex

Designers of electronic devices and telecommunications equipment have used different methods (e.g., three-dimensional chip architecture, comprised of a vertically integrated stack of chips) to increase the number of transistors on integrated circuits. These latest chips generate excessive amounts of heat and thus can reach unacceptably high temperatures. In this context, this research aims to develop thermally conductive liquid crystal polymer (LCP)-hexagonal boron nitride (hBN) composite films to replace the traditionally used Kapton films that satisfy the electrical insulation requirements for the attachment of heat sinks to the chips without compromising the heat dissipation performance. Experimental simulations show that the maximum temperature of the heater mounted with a hybrid heat sink reduced with increased hBN content. Furthermore, a sufficient slow cooling process in the fabrication stage of the LCP/hBN composite films promotes the fibrillation of LCP matrix, leading to highly ordered structure and promoted the composite’s heat dissipation ability.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.322
Teacher spread0.220 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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