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Record W2121068127 · doi:10.1002/app.37941

Synergistic effects of hybrid fillers on the development of thermally conductive polyphenylene sulfide composites

2012· article· en· W2121068127 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Polymer Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialFiller (materials)SulfideThermal conductivityBoron nitrideCarbon blackInterconnectivityGrapheneElectrical conductorCarbon fibersComposite numberNanotechnology

Abstract

fetched live from OpenAlex

Abstract The future of integrated circuits with three‐dimensional chip architecture hinges on the development of practical solutions for the management of excessive amounts of heat generation. This requires new polymer–matrix composites (PMCs), with good processibility, high effective thermal conductivity ( k eff ), and low but tailored electrical conductivity (σ). This article explores the synergy of hybrid fillers: (i) hexagonal boron nitride (hBN) platelets with different sizes and shapes; (ii) hBN platelets with carbon‐based fillers promoting the k eff of the polyphenylene sulfide (PPS) composites. It explores the promotion of interconnectivity among the fillers in the PPS matrix, leading to higher k eff , by the uses of hybrid fillers. It discusses using carbon‐based fillers as secondary fillers to tailor the PMCs' σ. Finally, it presents the effects of hybrid fillers on the PMCs' coefficient of thermal expansion. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.228
Teacher spread0.213 · 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