Thermal and Electrical Properties of Graphene-based Polymer Nanocomposite Foams
Bibliographic record
Abstract
Recently, multifunctional polymer-graphene nanoplatelet (GnP) composites have demonstrated great promise as next-generation materials for energy management and storage, electromagnetic interference (EMI) shielding and heat dissipation components in electronic industries. However, the practical underpinning needed to economically manufacture graphene-based polymer composites is missing. Therefore, this dissertation aims to demonstrate how some of the challenges for efficient manufacturing of functional polymer composites, can be strategically tackled by using supercritical fluid (SCF)-treatment and physical foaming technologies. In this PhD research, an industrial-scale technique for in situ exfoliation and dispersion of GnP in polymer matrices was developed and invented. This thesis also developed an in-depth understanding of the effects of cellular structures, GnPs’ orientation, arrangement, and exfoliation on the thermal/electrical conductivity, percolation threshold, dielectric performance, and EMI shielding effectiveness of the graphene-based polymer composites. In particular, it was demonstrated how SCF−treatment and physical foaming can significantly enhance thermal conductivity of polymer-GnP composites. The SCF-treatment and physical foaming exfoliated the GnPs in situ and microscopically tailored the nanocomposites’ structure to enhance the thermal conductivity. The research findings in this thesis have also demonstrated that the introduction of foaming and microcellular structure can substantially increase the electrical conductivity, EMI shielding effectiveness and can decrease the percolation threshold of the polymer-GnP composites. This research also presented a facile technique for manufacturing a new class of ultralight polymer-GnP composite foams with excellent dielectric performance. The generation of a microcellular structure provided a unique parallel-plate arrangement of GnPs around the cell walls. This significantly increased the real permittivity and decreased the dielectric loss. This dissertation developed a fundamental understanding of structure-property relationships and new routes to microscopically engineer the structures and properties of graphene-based polymer composites for various application such as heat management (heat sink materials), EMI shielding, energy storage and capacitors (dielectric materials).
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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".