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Record W2435858704 · doi:10.7567/apex.9.075002

Thermal conductivity of vertically aligned boron nitride nanotubes

2016· article· en· W2435858704 on OpenAlexaff
B. E. Belkerk, Amine Achour, Dongyan Zhang, Salah Sahli, M-Abdou Djouadi, Yoke Khin Yap

Bibliographic record

VenueApplied Physics Express · 2016
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersMichigan Technological UniversityCHIST-ERAAgence Nationale de la RechercheNational Science Foundation
KeywordsBoron nitrideMaterials scienceThermal conductivityChemical vapor depositionConductivityComposite materialNitrideElectrical conductorThermalChemical engineeringNanotechnologyLayer (electronics)ChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract For the first time, we report the thermal conductivity of vertically aligned boron nitride nanotube (BNNT) films produced by catalytic chemical vapor deposition. High-quality BNNTs were synthesized at 1200 °C on fused silica substrates precoated with Pt thin-film thermometers. The thermal conductivity of the BNNTs was measured at room temperature by using a pulsed photothermal technique. The apparent thermal conductivity of the BNNT coatings increased from 55 to 170 W m−1 K−1 when the thickness increased from 10 to 28 µm, while the thermal conductivity attained a value as high as 2400 W m−1 K−1. These results suggested that BNNTs, which are highly thermally conductive, but electrically insulating, are promising materials with unique properties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations59
Published2016
Admission routes1
Has abstractyes

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