Boosting the Heat Dissipation Performance of Graphene/Polyimide Flexible Carbon Film via Enhanced Through‐Plane Conductivity of 3D Hybridized Structure
Bibliographic record
Abstract
Abstract The development of materials with efficient heat dissipation capability has become essential for next‐generation integrated electronics and flexible smart devices. Here, a 3D hybridized carbon film with graphene nanowrinkles and microhinge structures by a simple solution dip‐coating technique using graphene oxide (GO) on polyimide (PI) skeletons, followed by high‐temperature annealing, is constructed. Such a design provides this graphitized GO/PI (g‐GO/PI) film with superflexibility and ultrahigh thermal conductivity in the through‐plane (150 ± 7 W m‐1 K‐1) and in‐plane (1428 ± 64 W m‐1 K‐1) directions. Its superior thermal management capability compared with aluminum foil is also revealed by proving its benefit as a thermal interface material. More importantly, by coupling the hypermetallic thermal conductivity in two directions, a novel type of carbon film origami heat sink is proposed and demonstrated, outperforming copper foil in terms of heat extraction and heat transfer for high‐power devices. The hypermetallic heat dissipation performance of g‐GO/PI carbon film not only shows its promising application as an emerging thermal management material, but also provides a facile and feasible route for the design of next‐generation heat dissipation components for high‐power flexible smart devices.
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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".