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Record W2970608502

Thermal and Electrical Properties of Graphene-based Polymer Nanocomposite Foams

2019· dissertation· en· W2970608502 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneNanocompositeMaterials scienceComposite materialPolymer nanocompositeThermalPolymerNanotechnologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.223
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it