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Record W2151105731 · doi:10.1002/app.40631

Simultaneous polypropylene functionalization and nanoclay dispersion in PP/Clay nanocomposites using ultrasound

2014· article· en· W2151105731 on OpenAlexaff
J. G. Martínez‐Colunga, S. Sánchez‐Valdés, Libertad Muñoz‐Jiménez, E. Ramírez‐Vargas, M.C. Ibarra-Alonso, Tomás Lozano, Pierre G. Lafleur

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsPolytechnique Montréal
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsMaterials scienceSurface modificationPolypropyleneNanocompositeMaleic anhydrideSonicationDispersion (optics)MontmorilloniteComposite materialPlastics extrusionPolymerCompatibilizationChemical engineeringPolymer blendCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT Polypropylene nanocomposite materials were prepared with 5 and 10 wt % cloisite C20A clay, jointly with 0.6 and 1.2 wt % of maleic anhydride (MA) for the simultaneous polymer functionalization and clay dispersion in a twin screw extruder assisted with ultrasonic irradiation, using different sonication intensities (231, 347, and 462 W, which correspond to 30%, 45%, and 60% of the maximum instrument intensity, “770 W”) all in a single‐step operation. The MA polymer functionalization was followed by FTIR spectroscopy and determined by titration. The increase in modulus of the obtained PP/Clay nanocomposites was attributed to the greater dispersion level, presumably achieved becuase of the joint application of the PP–Clay compatibilization with MA and the sonication during processing in a twin screw extruder. The greater level of clay dispersion was verified by the displacement of the XRD diffraction peak to lower angles, indicating an intercalated‐exfoliated structure that was corroborated by STEM. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131 , 40631.

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 score0.005
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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