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Record W2526743759 · doi:10.1177/026248930902800101

Foaming Cyclo-Olefin Copolymers with Carbon Dioxide

2009· article· en· W2526743759 on OpenAlexaff
Richard Gendron, Michel Champagne, J. Tatibouët, Martin Bureau

Bibliographic record

VenueCellular Polymers · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceGlass transitionCopolymerComposite materialMonomerPlasticizerNorborneneSorptionOlefin fiberSolubilityAutoclaveAmorphous solidRheologyAbsorption of waterSupercritical carbon dioxideChemical engineeringPolymer chemistryPolymerSupercritical fluidOrganic chemistry

Abstract

fetched live from OpenAlex

Cyclo-olefin copolymers (COC) based on ethylene-norbornene (E-NB) structures have been investigated with respect to their foamability. COC present interesting characteristics, such as extremely low water absorption, excellent water vapor barrier properties, high strength and very good electrical insulating properties. Varying the ratio of E versus NB monomers enables to tune the glass transition temperature (T g ) over a wide range. Due to the rigid ring structure of NB, the resin remains amorphous. This study presents basic information related to the foam processing of different grades of COC resins with T g varying between 80 and 130 °C, adding a special emphasis on process-relevant material characteristics: rheology, solubility and plasticization. CO 2 -laden COC sheets, where the gas sorption was obtained using high pressure autoclave, were foamed using a biaxial stretcher. The impact of selected processing parameters (temperature, time of pre-heating, speed of deformation, magnitude of stretching) on the microcellular foam morphology and density will be reported throughout this paper.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.192
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes1
Has abstractyes

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