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Record W2094149615 · doi:10.1002/pc.20863

Synthesis and properties of polystyrene‐<i>g</i>‐mSiO<sub>2</sub> filled polypropylene nanocomposites

2009· article· en· W2094149615 on OpenAlexaff
Aiyun Cai, Aiping Zhu, Sheng Dai

Bibliographic record

VenuePolymer Composites · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceNanocompositePolystyrenePolypropyleneCrystallizationDifferential scanning calorimetryChemical engineeringDynamic mechanical analysisPolymerizationComposite materialPolymer chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract Hydrophobically modified nanosilica was prepared from tetraethoxysilane (TEOS) and γ‐methacryloxypropyltrimethoxysilane (MPS) by a two step sol‐gel process. The polystyrene‐grafted‐modified nanosilica (PS‐ g ‐mSiO 2 ) hybrid particles were prepared by grafting polystyrene onto the resulting hydrophobically modified nanosilica by dispersion polymerization. The hybrid nanoparticles were subsequently used as the filler to fabricate polypropyrene (PP) nanocomposites. The crystallization kinetics, crystal morphology and crystallization phase component of PS‐ g ‐mSiO 2 /PP nanocomposite were studied using a differential scanning calorimeter (DSC), polarizing optical microscopy (POM) and X‐ray diffraction (XRD). Crystallization half life ( t 1/ 2 ) decreased, while the Arami exponent ( n ) of PS‐ g ‐mSiO 2 /PP nanocomposite increased compared with that of virgin PP. A rheological study allowed the unambiguous characterization of the dispersibility of nanosilicas in PS‐ g ‐mSiO 2 /PP nanocomposite. The storage modulus, melt viscosity and the elongation to break of the PS‐ g ‐mSiO 2 /PP nanocomposite were found to be strongly dependent on the grafting of PS on nanosilicas. POLYM. COMPOS., 2010. © 2009 Society of Plastics Engineers

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 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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0000.000

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 head0.199
Teacher spread0.187 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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