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Record W4394892212 · doi:10.3390/ma17081855

Rheological and Functional Properties of Mechanically Recycled Post-Consumer Rigid Polyethylene Packaging Waste

2024· article· en· W4394892212 on OpenAlexaff
Ezgi Ceren Boz Noyan, Franziska Rehle, Antal Boldizar

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRheologyMaterials sciencePolyethyleneComposite materialWaste managementEngineering

Abstract

fetched live from OpenAlex

The properties of recycled post-consumer rigid polyethylene packaging waste were studied, using sorted waste washed in the laboratory with water alone and with added detergent, and compared with large-scale high-intensity washed flakes. The washed flakes were compounded using three different temperature profiles in a twin-screw extruder and then injection molded. A higher compounding temperature reduced the thermo-oxidative stability, the average molecular mass, and the viscosity of the samples. Rheological measurements suggested that changes in chain branching occurred at different compounding temperatures. The strength and the elongation at break were also influenced by the compounding temperature in both the molten and solid states. Detergent washing maintained the thermo-oxidative stability in contrast to washing with water. The large-scale washed samples had a relatively high thermo-oxidative stability, a higher melt elasticity, and a lower elongation at break in both the molten and solid states than the laboratory-scale washed samples. The thermal properties, melt elasticity, Young's modulus, yield stress, and yield strain of the samples were not, however, significantly affected by either the compounding temperature or the washing medium and intensity. The results indicated that recycled post-consumer rigid polyethylene packaging waste has properties that can support further applications in new products.

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 categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.221
Teacher spread0.195 · 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.

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

Citations7
Published2024
Admission routes1
Has abstractyes

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