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Record W4413782748 · doi:10.1177/08927057251371578

Thermally conductive and electrically insulative polypropylene composites based on polymethylsilsesquioxane microparticles

2025· article· en· W4413782748 on OpenAlexafffund
Khadim MBOUP, Fouad Erchiqui, Karima Ben Hamou, Abdessamad Baatti, Denis Rodrigue

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversité LavalUniversité de MonctonUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialPolypropyleneElectrical conductorElectrically conductive

Abstract

fetched live from OpenAlex

This study investigates the development of polypropylene (PP) composites reinforced with polymethylsilsesquioxane (PMSQ) microparticles at varying concentrations (0, 5, 10, and 15 wt%). The composites were produced via melt blending to evaluate the effect of PMSQ on their thermal, thermo-mechanical, and electrical properties. Differential scanning calorimetry (DSC) revealed slight changes in melting temperature, crystallization temperature, and crystallinity level with PMSQ addition. Thermogravimetric analysis (TGA) showed improved thermal stability, with residual mass increasing proportionally to PMSQ content. Dynamic mechanical analysis (DMA) indicated enhanced stiffness, as showed by a 32% increasing in storage modulus at 15 wt% PMSQ. However, the glass transition temperature remained unchanged. The thermal conductivity was improved by 20% with 10 wt% PMSQ, coupled with a tenfold increase in electrical resistivity. These results suggest that PP/PMSQ composites are promising candidates for applications requiring efficient heat dissipation and high electrical insulation, such as in electrical and electronic systems.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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