Highly Thermoconductive, Thermostable, and Super‐Flexible Film by Engineering 1D Rigid Rod‐Like Aramid Nanofiber/2D Boron Nitride Nanosheets
Bibliographic record
Abstract
Abstract Polymer‐based thermal management materials have many irreplaceable advantages not found in metals or ceramics, such as easy processing, low density, and excellent flexibility. However, their limited thermal conductivity and unsatisfactory resistance to elevated temperatures (<200 °C) still prevent effective heat dissipation during applications with high‐temperature conditions or powerful operation. Therefore, herein highly thermoconductive and thermostable polymer nanocomposite films prepared by engineering 1D aramid nanofiber (ANF) with worm‐like microscopic morphologies into rigid rod‐like structures with 2D boron nitride nanosheets (BNNS) are reported. With no coils or entanglements, the rigid polymer chain enables a well‐packed crystalline structure resulting in a 20‐fold (or greater) increase in axial thermal conductivity. Additionally, strong interfacial interactions between the weaved ANF rod and the stacked BNNS facilitate efficient heat flux through the 1D/2D configuration. Hence, unprecedented in‐plane thermal conductivities as high as 46.7 W m −1 K −1 can be achieved at only 30 wt% BNNS loading, a value of 137% greater than that of a worm‐like ANF/BNNS counterpart. Moreover, the thermally stable nanocomposite films with light weight (28.9 W m −1 K −1 /10 3 (kg m −3 )) and high strength (>100 MPa, 450 °C) enable effective thermal management for microelectrodes operating at temperatures beyond 200 °C.
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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".