Recent Progress in the Study of Thermal Properties and Tribological Behaviors of Hexagonal Boron Nitride-Reinforced Composites
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".