Conjoint Effect of Boron Nitride and Surface-Enhanced Flake Graphite in Thermal Conductivity of Thermally Conductive Grease
Bibliographic record
Abstract
The next generation of high-power electronic devices is expected to exhibit improved heat dissipation capabilities despite their smaller size. Current studies have investigated the utilization of hybrid fillers, our study introduces a novel approach by combining boron nitride (BN) and surface-enhanced flake graphite (G), both of which possess a platelet-like structure, to develop a thermally conductive grease. The grease shows an exceptionally high thermal conductivity of 2.21 W/mK and an extremely low electrical conductivity of 7.3×10-6 S/m. The viscosity of the grease is measured at 149 Pa·s. By incorporating hybrid fillers with a significantly high aspect ratio into EPON 828, a notable reduction of interfacial thermal resistance is observed, which is attributed to the formation of an effective pathway for phonon transfer facilitated by the unique characteristics of the hybrid fillers. Various theoretical models are employed to corroborate the experimental data, which facilitates substantiating the fundamental principles underlying the enhanced thermal conductivity of the prepared thermal grease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".