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Record W2017214162 · doi:10.1002/pen.21824

Preparation of thermoplastic elastomer nanocomposites based on polyamide‐6/polyepichlorohydrin‐<i>co</i>‐ethylene oxide

2010· article· en· W2017214162 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymer Engineering and Science · 2010
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials sciencePolyamideOrganoclayNanocompositeComposite materialNatural rubberThermoplastic elastomerElastomerUltimate tensile strengthPolymerCopolymer

Abstract

fetched live from OpenAlex

Abstract This work is aimed at determining the effect of nanoclay and polyepichlorohydrin‐ co ‐ethylene oxide (ECO) content on the microstructure and mechanical properties of PA6/ECO thermoplastic elastomers (TPEs). TPE nanocomposites were prepared in a laboratory mixer using polyamide 6 (PA6), ECO, and an organoclay by a two‐step melt mixing process. First, the PA6 was melt blended with Cloisite 30B and then mixed by ECO rubber. X‐ray diffraction results and transmission electron microscopy image showed that the nanoclay platelets were nearly exfoliated in both the phases. The SEM photomicrograph of PA6 with ECO showed that the elastomer particles are dispersed throughout the polyamide matrix and the size of rubber particles is less than 3 μm. Introduction of organoclay in the PA6 matrix increased the size of dispersed rubber particles in comparison with the unfilled but otherwise similar blends. The nanoscale dimension of the dispersed clay results in an improvement of the tensile modulus of the nanocomposites. POLYM. ENG. SCI., 2011. © 2010 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.

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 score0.042
Threshold uncertainty score0.683

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 communication0.0000.000
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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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