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Record W2888750393 · doi:10.1021/acsami.8b10745

Enhanced Electrical and Electromagnetic Interference Shielding Properties of Polymer–Graphene Nanoplatelet Composites Fabricated via Supercritical-Fluid Treatment and Physical Foaming

2018· article· en· W2888750393 on OpenAlexafffund
Mahdi Hamidinejad, Biao Zhao, Azadeh Zandieh, Nima Moghimian, Tobin Filleter, Chul B. Park

Bibliographic record

VenueACS Applied Materials & Interfaces · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectromagnetic wave absorption materials
Canadian institutionsNanoXplore (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialPercolation thresholdHigh-density polyethyleneNanocompositeSupercritical fluidComposite numberMolding (decorative)DielectricPercolation (cognitive psychology)Blowing agentElectrical resistivity and conductivityViscosityPolyethylenePolyurethane

Abstract

fetched live from OpenAlex

Lightweight high-density polyethylene (HDPE)-graphene nanoplatelet (GnP) composite foams were fabricated via a supercritical-fluid (SCF) treatment and physical foaming in an injection-molding process. We demonstrated that the introduction of a microcellular structure can substantially increase the electrical conductivity and can decrease the percolation threshold of the polymer-GnP composites. The nanocomposite foams had a significantly higher electrical conductivity, a higher dielectric constant, a higher electromagnetic interference (EMI) shielding effectiveness (SE), and a lower percolation threshold compared to their regular injection-molded counterparts. The SCF treatment and foaming exfoliated the GnPs in situ during the fabrication process. This process also changed the GnP's flow-induced arrangement by reducing the melt viscosity and cellular growth. Moreover, the generation of a cellular structure rearranged the GnPs to be mainly perpendicular to the radial direction of the bubble growth. This enhanced the GnP's interconnectivity and produced a unique GnP arrangement around the cells. Therefore, the through-plane conductivity increased up to a maximum of 9 orders of magnitude and the percolation threshold decreased by up to 62%. The lightweight injection-molded nanocomposite foams of 9.8 vol % GnP exhibited a real permittivity of ε' = 106.4, which was superior to that of their regular injection-molded (ε' = 6.2). A maximum K-band EMI SE of 31.6 dB was achieved in HDPE-19 vol % GnP composite foams, which was 45% higher than that of the solid counterpart. In addition, the physical foaming reduced the density of the HDPE-GnP foams by up to 26%. Therefore, the fabricated polymer-GnP nanocomposite foams in this study pointed toward the further development of lightweight and conductive polymer-GnP composites with tailored properties.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations196
Published2018
Admission routes2
Has abstractyes

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