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Record W2003798159 · doi:10.1002/pen.23334

Morphological aspects of injected polypropylene/clay nanocomposite materials

2012· article· en· W2003798159 on OpenAlexaff
Hend Ben Hadj Salah, Hachmi Ben Daly, Johanne Denault, Florence Perrin

Bibliographic record

VenuePolymer Engineering and Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPolypropyleneMaterials scienceNanocompositeComposite material

Abstract

fetched live from OpenAlex

Abstract Polypropylene (PP) clay nanocomposites were injection‐molded using two different coupling agents based on maleic anhydride‐grafted PP (MA‐ g ‐PP) and two clay loadings. The morphological aspects of these materials were studied by depth profiling. Molecular chain and clay orientations were characterized using attenuated total reflectance‐infrared analysis and transmission electron microscopy (TEM). Both clay platelets and PP molecular chain orientations were found to decrease from the surface toward the core of the injection–molded specimens. Clay intercalation, characterized by both complementary X‐ray diffraction and TEM, was found to be significantly influenced by both the characteristics of the coupling agent used and the type of residual stresses generated at each layer across the thickness of the injection‐molded parts. The use of low‐molecular weight ( M w ) MA‐ g ‐PP led to a uniform intercalation but with no further exfoliation. The use of higher molecular weight MA‐ g ‐PP led to a heterogeneous intercalation with some signs of exfoliation. The crystallization behavior of PP clay nanocomposites studied by differential scanning calorimetry showed an increase in the level of crystallinity from the surface to the core of the specimens; these results were also confirmed by scanning electron microscopy. POLYM. ENG. SCI., 2013. © 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)0.001
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 score0.009
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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