Aerogel Perfusion-Prepared h-BN/CNF Composite Film with Multiple Thermally Conductive Pathways and High Thermal Conductivity
Bibliographic record
Abstract
Hexagonal boron nitride (h-BN)-based heat-spreading materials have drawn considerable attention in electronic diaphragm and packaging fields because of their high thermal conductivity and desired electrical insulation properties. However, the traditional approach to fabricate thermally conductive composites usually suffers from low thermal conductivity, and cannot meet the requirement of thermal management. In this work, novel h-BN/cellulose-nano fiber (CNF) composite films with excellent thermal conductivity in through plane and electrical insulation properties are fabricated via an innovative process, i.e., the perfusion of h-BN into porous three dimensional (3D) CNF aerogel skeleton to form the h-BN thermally conductive pathways by filling the CNF aerogel voids. When at an h-BN loading of 9.51 vol %, the thermal conductivity of h-BN/CNF aerogel perfusion composite film is 1.488 W·m−1·K−1 at through plane, an increase by 260.3%. The volume resistivity is 3.83 × 1014 Ω·cm, superior to that of synthetic polymer materials (about 109~1013 Ω·cm). Therefore, the resulting h-BN/CNF film is very promising to replace the traditional synthetic polymer materials for a broad spectrum of applications, including the field of electronics.
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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) | 0.001 | 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".