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Record W3049612802 · doi:10.3390/jcs4030116

Recent Progress in the Study of Thermal Properties and Tribological Behaviors of Hexagonal Boron Nitride-Reinforced Composites

2020· article· en· W3049612802 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

VenueJournal of Composites Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceThermal conductivityBoron nitrideHexagonal boron nitrideComposite materialTribologyComposite numberThermal expansionLubricationThermalNanotechnologyGraphene

Abstract

fetched live from OpenAlex

Ever-increasing significance of composite materials with high thermal conductivity, low thermal expansion coefficient and high optical bandgap over the last decade, have proved their indispensable roles in a wide range of applications. Hexagonal boron nitride (h-BN), a layered material having a high thermal conductivity along the planes and the band gap of 5.9 eV, has always been a promising candidate to provide superior heat transfer with minimal phonon scattering through the system. Hence, extensive researches have been devoted to improving the thermal conductivity of different matrices by using h-BN fillers. Apart from that, lubrication property of h-BN has also been extensively researched, demonstrating the effectivity of this layered structure in reduction of friction coefficient, increasing wear resistance and cost-effectivity of the process. Herein, an in-depth discussion of thermal and tribological properties of the reinforced composite by h-BN will be provided, focusing on the recent progress and future trends.

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)0.002
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 score0.060
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.275
Teacher spread0.219 · 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