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Record W4413128050 · doi:10.1177/08927057251368874

Effects of nanotitania on structure-dielectric properties of polypropylene blends

2025· article· en· W4413128050 on OpenAlexaff
N. A. Azrin, Noor Azlinda Ahmad, Kwan Yiew Lau, Mona Riza Mohd Esa, S. N. H. Kamarudin, Nor Hidayah Rahim, Mohd Aizam Talib

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsShared Health
FundersUniversiti Teknologi MalaysiaMinistry of Higher Education, Malaysia
KeywordsMaterials sciencePolypropyleneComposite materialDielectric

Abstract

fetched live from OpenAlex

Polypropylene (PP) satisfies many of the requirements as power cable insulation thanks to its high thermal stability, ability to withstand high voltages, and eco-friendly properties. Due to the high stiffness and brittleness of PP, the extrusion of PP as power cable insulation becomes difficult. Therefore, elastomers have been blended with PP to enhance PP’s flexibility. Nevertheless, one common issue arises when blending elastomer with PP is the degradation of PP/elastomer blends’ dielectric properties. Recently, nanodielectrics have drawn utmost attention as a promising material system for improving the overall performance of polymer-based insulating materials, but such a material system has yet to be pursued for PP blend materials. The current work therefore investigates the effects of titania (TiO 2 ) on the structure and dielectric properties of PP/ethylene-based elastomer (EBE) and PP/propylene-based elastomer (PBE). While adding high amounts of TiO 2 degrades the blend materials’ breakdown strength, the inclusion of small amounts (0.5 wt% and 1 wt%) of TiO 2 improves the breakdown strength of the blend materials by as much as 10%. Notably, the effect of TiO 2 is more apparent on PP/EBE system over PP/PBE system. Mechanisms underlying changes in the breakdown strength of the materials, under both alternating current (AC) and direct current (DC) fields, are analyzed.

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)0.000
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 score0.003
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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