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Record W1979574698 · doi:10.1002/app.22688

Surface modification of ultra‐high‐molecular‐weight polyethylene. II. Effect on the physicomechanical and tribological properties of ultra‐high‐molecular‐weight polyethylene/poly(ethylene terephthalate) composites

2005· article· en· W1979574698 on OpenAlexaff
Mostafa Rezaei, Shirzad Azadeh, Nadereh Golshan Ebrahimi, Marianna Kontopoulou

Bibliographic record

VenueJournal of Applied Polymer Science · 2005
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceComposite materialUltra-high-molecular-weight polyethylenePolyethyleneTribologyPolyethylene terephthalateSurface modificationPolymerSynthetic fiberDispersion (optics)Chromic acidEthyleneFiberChemical engineering

Abstract

fetched live from OpenAlex

Abstract We performed surface modification of ultra‐high‐molecular‐weight polyethylene (UHMWPE) through chromic acid etching, with the aim of improving the performance of its composites with poly(ethylene terephthalate) (PET) fibers. In this article, we report on the morphology and physicomechanical and tribological properties of modified UHMWPE/PET composites. Composites containing chemically modified UHMWPE had higher impact properties than those based on unmodified UHMWPE because of improved interfacial bonding between the polymer matrix and the fibers and better dispersion of the fibers within the modified UHMWPE matrix. Chemical modification of UHMWPE before the introduction of PET fibers resulted in composites exhibiting improved wear resistance compared to the base material and compared to unmodified UHMWPE/PET composites. On the basis of the morphological studies of worn samples, microploughing and fatigue failure associated with microcracking were identified as the principle wear mechanisms. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006

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 score0.019
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.244
Teacher spread0.231 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

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