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Record W1528933536 · doi:10.1002/pc.23662

Use of supercritical CO<sub>2</sub> as a dispersing agent in the preparation of a polymer‐layered silicate nanocomposite

2015· article· en· W1528933536 on OpenAlexaff
Jinling Liu, William R. Rodgers, Michael R. Thompson

Bibliographic record

VenuePolymer Composites · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOrganoclayMaterials scienceNanocompositeComposite materialCompoundingPolypropyleneRheologySupercritical fluidPolymerCrystallinityThermoplasticDispersion (optics)SilicateChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The usefulness of supercritical CO 2 (scCO 2 ) in the processing of thermoplastic olefin (TPO) nanocomposites was investigated using a simple extensional flow mixing device. An organoclay (Cloisite ® 20A), a maleated polypropylene (Polybond ® 3200), and a TPO polymer were selected for this study. The three components were combined in different manners to evaluate how scCO 2 influenced the microstructure of a final nanocomposite prepared by different mixing orders of the formulation components. The organoclay was examined in its as‐supplied state as well as after being conditioned under scCO 2 . Analyses of the nanocomposites by X‐ray diffraction, transmission electron microscopy, and parallel plate rheology techniques showed that inclusion of scCO 2 during melt compounding significantly contributed to disrupting the crystalline order in the structure of the original organoclay. However, the order by which the formulation components are combined under scCO 2 was important to clay dispersion. It was shown that making use of the proper combination order led to an improvement in the modulus of the resulting nanocomposites as measured by rheology. POLYM. COMPOS., 38:987–995, 2017. © 2015 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 score0.008
Threshold uncertainty score0.937

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 communication0.0000.001
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 head0.052
GPT teacher head0.290
Teacher spread0.239 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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