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Record W4403850888 · doi:10.18280/acsm.480513

Effect of High Processing Temperature on the Rheological and Morphological Properties of Recycled Polypropylene

2024· article· en· W4403850888 on OpenAlexvenueno aff
Noor Alhuda Sabah Jassim, Marwa A. Anber, Salah M S Alhar

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsnot available
Fundersnot available
KeywordsPolypropyleneRheologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This study investigates the effect of high processing temperatures used in the medical syringe factory in Iraq on some rheological and morphological properties of polypropylene.Ten samples consisting of 50% virgin (pure) PP and 50% recycled PP were processed.The mold temperature was varied in the range of 190℃ to 210℃, while maintaining the pressure and secondary time at 78 bar and 0.5 seconds, respectively.The results showed that the density decreased by 45% as the injection temperature increased.In contrast, the Melt Flow Index (MFI) increased by 60% with rising injection temperatures.The results also indicated a severe effect of high processing temperatures on the samples, which was evident in the FTIR test, where the intensity of the peaks changed significantly compared to virgin PP.The DSC test revealed that the Tg value increased to 15.1℃ for recycled PP, compared to 9.8℃ for the virgin sample.

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 head0.032
GPT teacher head0.266
Teacher spread0.234 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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