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Record W2054775049 · doi:10.3139/217.2026

Rheological Modification of LLDPE Through Reactive Processing with Peroxide

2008· article· en· W2054775049 on OpenAlexaff
Patchara Tasanatanachai, Costas Tzoganakis, Rathanawan Magaraphan

Bibliographic record

VenueInternational Polymer Processing · 2008
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Waterloo
FundersRoyal Golden Jubilee (RGJ) Ph.D. Programme
KeywordsPeroxideRheologyMaterials scienceBranching (polymer chemistry)Reactive extrusionPolymerPolymerizationMixing (physics)PolyethyleneRheometerChemical engineeringOrganic peroxideLinear low-density polyethyleneComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract The presence of long-chain branching provides advantageous properties to a molten polymer in film production, especially for a fast growing commodity plastic such as polyethylene. Besides the innovation of polymerization, the most attractive method to tailor the molecular structure is peroxide modification via reactive processing which is the post-reactor technique. By this process, it can achieve the enhancement of ability of the melt to be drawn or stretched during the film processing due to branching. However, to avoid the undesired excess of macroscopic molecular network, which may cause defects in the film product, a small quantity of peroxide initiator was used to modify the base resin. The rheological properties, as related to molecular characteristics, were investigated using a capillary rheometer. Rheological analysis of the products modified by different peroxide addition methods was of interest as changes in rheological properties depend markedly on the efficiency of incorporating and mixing the peroxide into the polymer. Process parameters (including mixing temperature, peroxide quantity, and mixing speed), which appear to be responsible for the molecular characteristics alteration, were considered in order to produce materials with preferred rheological properties.

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 score0.071
Threshold uncertainty score0.463

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 head0.052
GPT teacher head0.284
Teacher spread0.232 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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