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Record W2091485885 · doi:10.1002/pc.23265

Synthesis, structure, and properties of high‐impact polystyrene/octavinyl polyhedral oligomeric silsesquioxane nanocomposites

2014· article· en· W2091485885 on OpenAlexaff
Kun Hu, Zhong‐Kai Cui, Yongliang Yuan, Qixin Zhuang, Tongxin Wang, Xiaoyun Liu, Zhewen Han

Bibliographic record

VenuePolymer Composites · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsUniversité de Montréal
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsSilsesquioxaneMaterials scienceNanocompositeFourier transform infrared spectroscopyPolystyreneUltimate tensile strengthComposite materialThermal decompositionThermal stabilityIzod impact strength testPolymerizationPolymerChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The organic–inorganic hybrid nanocomposites from high‐impact polystyrene/octavinyl polyhedral oligomeric silsesquioxane (HIPS/POSS) containing various percentages of POSS were prepared by free radical polymerization and characterized by Fourier transform infrared spectroscopy (FTIR), 1 H‐NMR, thermal gravity analysis (TGA), X‐ray diffraction (XRD), and transmission electron microscopy (TEM). The octavinyl POSS has formed covalent bond connected PS‐POSS hybrid with polystyrene. POSS can well disperse in the composites at the composition of 0.5 and 1 wt%. The mechanical properties and thermostability of HIPS/POSS nanocomposites were significantly improved. The tensile strength, the izod impact strength, and the elongation at break of the nanocomposite containing 1 wt% of POSS was increased, respectively, by 15.73%, 75.62%, and 72.71% in comparison with pristine HIPS. The thermal decomposition temperature of HIPS/POSS (1 wt% of POSS) was 33°C higher than that of pristine HIPS. The HIPS/POSS nanocomposites showed great potential for applications in many fields, such as electric appliance and automotive trim. POLYM. COMPOS. 37:1049–1055, 2016. © 2014 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 score0.008
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.212
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.

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

Citations21
Published2014
Admission routes1
Has abstractyes

Explore more

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