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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.007
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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