A Continuous 3D-Graphene Network to Overcome Threshold Issues and Contact Resistance in Thermally Conductive Graphene Nanocomposites
Bibliographic record
Abstract
In order to overcome thermal resistance issues in polymeric matrix composites, self-standing graphene aerogels were synthetized and infiltrated with an epoxy resin, in order to create conductive preferential pathways through which heat can be easily transported. These continuous highly thermally conductive 3D-structures show, due to the high interconnection degree of graphene flakes, enhanced transport properties. Two kinds of aerogels were investigated, obtained by hydrothermal synthesis (HS) and ice-templated direct freeze synthesis (DFS). Following HS method an isotropic structure is obtained, and following DFS method instead an anisotropic arrangement of graphene flakes results. The density of the structure can be tuned leading to a different amount of graphene inside the final composite. The residual oxygen, known to be detrimental to thermal properties, was removed by thermal treatment before the infiltration process. With 1,25 wt.% of graphene, using HS method, the thermal conductivity of the polymeric resin was increased by 80%, suggesting that this technique is a valid route to improve the thermal performance of graphene-based composites. When preferential orientation of the filler was present (DFS case), thermal conductivity was increased more than 25% with a graphene content of only 0,27 wt.%, demonstrating that oriented structures can further improve the thermal transport efficiency.
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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".