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Record W1978640465 · doi:10.3139/217.2575

Effect of Blend Ratio of h-LLDPE and LDPE on Tear Properties of Blown Films

2012· article· en· W1978640465 on OpenAlexaff
Khaled Mezghani, Sarfaraz Ahmed Furquan, Seyed H. Tabatabaei, Abdellah Ajji

Bibliographic record

VenueInternational Polymer Processing · 2012
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsPolytechnique Montréal
FundersSaudi Basic Industries CorporationKing Abdulaziz City for Science and TechnologyKing Fahd University of Petroleum and Minerals
KeywordsLow-density polyethyleneLinear low-density polyethyleneMaterials scienceComposite materialTear resistancePolymerImpact resistancePolymer blendPlastics extrusionUltimate tensile strength

Abstract

fetched live from OpenAlex

Abstract In the present study, blown films of h-LLDPE, LDPE, and their blends were produced using a twin screw extruder. The tear properties of all films were determined in the machine direction (MD) and the transverse direction (TD). On one hand, similar values of the MD tear-resistance for the two virgin polymers, h-LLDPE and LDPE, were measured to be 100 and 120 kN/m, respectively. On the other hand, the TD tear-resistance value of h-LLDPE was 180 kN/m, four times higher than that of LDPE, 45 kN/m. It was observed that small additions of LDPE (5 to 20 wt.%) to h-LLDPE produced blends with better TD tear-resistance films, 400 kN/m. The main reason for this large increase in tear-resistance was attributed to the morphological changes induced by the addition of the LDPE polymer, as shown by SEM, FTIR, and birefringence techniques.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.219
Teacher spread0.211 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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