MétaCan
Menu
Back to cohort
Record W2429168450 · doi:10.1002/pen.24363

Effects of Recycling on the Mechanical Behavior of Polypropylene at Room Temperature Through Statistical Analysis Method

2016· article· en· W2429168450 on OpenAlexafffund
Fatemeh Khademi, Yongsheng Ma, Cagri Ayranci, Keith Choi, Kajsa Duke

Bibliographic record

VenuePolymer Engineering and Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsPolypropyleneMaterials scienceComposite materialAnnealing (glass)ToughnessModulusYoung's modulusFiber

Abstract

fetched live from OpenAlex

This article reports the effects of recycled material percentage, annealing conditions, and glass fiber percentage on the mechanical behavior of injection molded polypropylene samples. Specimens were prepared with different percentages of recycled material ranging from 0 to 100%. Two groups of samples, i.e., non‐annealed and annealed at 150°C, were tested to investigate annealing effects. The effects of adding fiber (0–7.5%) to specimens was also investigated. It was found that increasing the amount of recycled material improves the material properties in a non‐linear trend. Annealing had a significant positive effect on both non‐fiber‐added and fiber‐added samples: it improved the yield stress of non‐reinforced polypropylene samples by more than 10% and their Young's modulus by about 50%. Fiber‐added materials showed more variability, and adding fiber also improved the Young's modulus and the yield stress of the samples by about 50%. The results indicate that the three factors investigated improved toughness of the injected polypropylene samples; however the effects are not significant. The study findings reveal that using recycled polypropylene has no significant effect on the material properties of polypropylene. POLYM. ENG. SCI., 56:1283–1290, 2016 © 2016 Society of Plastics Engineers

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 score0.113
Threshold uncertainty score0.186

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)0.0000.000

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 head0.006
GPT teacher head0.224
Teacher spread0.219 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePolymer Engineering and ScienceSame topicMicroplastics and Plastic PollutionFrench-language works237,207