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Record W1964253477 · doi:10.1002/app.29916

Preparation and thermal and thermo‐oxidative stability of vinylidene chloride‐<i>co</i>‐vinyl chloride copolymer/synthetic hectorite nanocomposites

2009· article· en· W1964253477 on OpenAlexaff
Tar‐Hwa Hsieh, Ko‐Shan Ho, Hsiaotao T. Bi, Jan‐Kuan Hung, Yu‐Kai Han, Shin‐Shiao Yang, Yu‐Chen Chang

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHectoriteNanocompositeThermal stabilityCopolymerMaterials sciencePolymer chemistryTernary operationChemical engineeringVinyl chlorideChemistryComposite materialOrganic chemistryPolymerMontmorillonite

Abstract

fetched live from OpenAlex

Abstract Poly(vinylidene chloride‐ co ‐vinylchloride)/organically modified hectorite (VDC‐VC/SPN) nanocomposites were prepared by melt blending VDC‐VC copolymer with SPN in the presence of dioctyl phthalate, which acted as a plasticizer. As a result, the exfoliated structure was found in the VDC‐VC/SPN nanocomposites. In nitrogen atmosphere, VDC‐VC/SPN nanocomposites exhibited a single‐step thermal degradation. The thermal stability of VDC‐VC/SPN nanocomposites is significantly influenced by the SPN, which was modified with long alkyl ternary ammonium salt. In air atmosphere, VDC‐VC/SPN nanocomposites revealed a two‐step thermo‐oxidative degradation behavior. At the first degradation stage, the weight loss pattern is similar to that of VDC‐VC composites in nitrogen, in which the thermo‐oxidative stability of VDC‐VC/SPN nanocomposites is affected by the ternary ammonium salt and oxygen rather than its morphology. At the second degradation stage, both the enhanced thermo‐oxidative stability and the flame‐retardation ability of VDC‐VC composites are strongly and closely related to the morphology of nanocomposites. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci ,2009

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 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 score0.006
Threshold uncertainty score0.815

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

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