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Record W2086908499 · doi:10.1002/pc.22319

Preparation and properties of polyester nanocomposites: Effects of mixing

2012· article· en· W2086908499 on OpenAlexaff
Mahmoud Rajabian, Hamid Piroozfar, Mohammad Hosain Beheshty

Bibliographic record

VenuePolymer Composites · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceThermosetting polymerMixing (physics)NanocompositeComposite materialViscoelasticityMicrostructureThermoplasticPolyesterDispersion (optics)NanoparticleNanotechnology

Abstract

fetched live from OpenAlex

Abstract A multistep mixing technique to manufacture highly intercalated/exfoliated nanocomposites of layered silicates in thermosetting resins was developed and successfully tested up to 9% nanoparticle content. We investigated the influences of mixing conditions on linear and nonlinear viscoelastic properties of processed clays and polyester suspensions. The effects of shear and mechanical mixing on the state of dispersion and microstructure of the cured nanocomposites were examined by the XRD and TEM techniques. Transient viscosities and linear viscoelastic data for the nanocomposites demonstrated significant enhancement by increasing speed and time of mechanical mixing. The solid state characterization techniques confirm mixed to highly exfoliated and delaminated microstructures were attained by the proposed method. Our observations indicate that the multistage mechanical mixing following a stationary step can be used to produce thermosetting and thermoplastic nanoparticles with the improved properties. POLYM. COMPOS., 2012. © 2012 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 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 score0.073
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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