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Record W1988879444 · doi:10.5539/mas.v4n5p22

The Effect of Pressure-Induced Flow (PIF) Processing on the Thermal Stability and Mechanical Properties of Ultra-High Molecular Weight Polyethylene (UHMWPE) Gel

2010· article· en· W1988879444 on OpenAlexvenueno aff
Musa E. Babiker, Guangcheng . Wang, Sen Zhang, Tang Yi Fei, Huaiping Rong, Muhuo Yu

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceThermogravimetric analysisComposite materialThermal stabilityDynamic mechanical analysisScanning electron microscopeUltra-high-molecular-weight polyethylenePolyethyleneDifferential scanning calorimetryMicrostructurePolymerChemical engineering

Abstract

fetched live from OpenAlex

A new method using gel-swelling state and pressure-induced flow (PIF) will be presented for obtaining high-performance materials of ultra high molecular weight polyethylene (UHMWPE), which is available for the production of high-strength and high-modulus materials. The effect of PIF on the thermal stability and mechanical properties UHMWPE gel state has been investigated using four kinds of gel formed from 4, 6, 8 and 10% of UHMWPE. The microstructures of UHMWPE gel were studied by using scanning electron microscope (SEM). The thermal and mechanical properties of the UHMWPE gel were investigated by thermogravimetric analysis (TGA), diffraction scanning calorimetry (DSC), dynamic mechanical analysis (DMA) and the universal test machine (UTM). The images obtained by SEM present a gel-like layers structure of UHMWPE gel sheet as a result of the deformation and re-arrangement of the gel sheets during PIF process. From the DSC, DMA and TGA results, we found that the thermal stability of the UHMWPE gel increased as the UHMWPE content increased. The thermal stability and the increase of strength are discussed with respect to the effect of PIF deformation.

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)0.002
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 score0.020
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.215
Teacher spread0.201 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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