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Record W2135392111 · doi:10.3139/217.0028

Paste Extrusion of Polytetrafluoroethylene: Temperature, Blending and Processing Aid Effects

2006· article· en· W2135392111 on OpenAlexaff
Isaias Ochoa, Savvas G. Hatzikiriakos, Evan Mitsoulis

Bibliographic record

VenueInternational Polymer Processing · 2006
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExtrusionMaterials scienceComposite materialBoron nitridePolytetrafluoroethylenePlastics extrusion

Abstract

fetched live from OpenAlex

Abstract Blends of different PTFE fine powder resins have been prepared and extruded in an attempt of improving the PTFE paste extrusion process and/or the extrudates. The extrusion pressure and the mechanical properties of the extrudates were found to fall between those exhibited by the pure components. While this is an expectable behavior, it offers opportunities for controlling the degree of fibrillation needed in the final products by mixing highly fibrillated with low fibrillated PTFE resins. Boron nitride and organically modified montmorillonite clays (solid lubricants) are also tested as additives to the lubricants used in the PTFE paste extrusion in order to identify enhanced processing aids. It was found that the addition of boron nitride and clays increase the extrusion pressure but at the same time improve the mechanical properties of the final extrudates in most cases. This again offers possibilities for controlling the final mechanical properties by controlling the degree of fibrillation, i. e., by adding a small amount of solid lubricants to adjust pressure, fibrillation, and thus the final mechanical properties. Finally, it is shown that fibrillation depends on temperature. Moreover, the mechanical properties of extrudates increase as the temperature goes through its two transition temperatures of 19 °C and 30 °C that render PTFE particles more deformable and prone to fibrillation.

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 score0.177
Threshold uncertainty score0.726

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 communication0.0000.001
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 head0.226
Teacher spread0.218 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

Explore more

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