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Record W4401008110 · doi:10.22063/poj.2022.3134.1212

Foam rotational molding of hybrid polyethylene nanocomposites: synergistic effect of microtalc and nanoclay

2022· article· en· W4401008110 on OpenAlexaff
Saeed Karimzadeh, Taher Azdast, Rezgar Hasanzadeh, Milad Moradian, Hamidreza Akrami

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanocompositeComposite materialPolyethyleneMaterials scienceMolding (decorative)

Abstract

fetched live from OpenAlex

Rotational molding is a process used to produce seamless, one-piece, and hollow polymeric parts. Foam rotational molding has recently become an increasingly important process in the foam industry. However, foam rotational molding is still a challenging process to fabricate polymeric foams. The focus of this manuscript was to assess the effect of material parameters on the foam properties of samples produced by rotational molding. Rotational molding experiments were performed on a laboratory-scale two-axis rotational machine, designed and manufactured by the authors. The effects of microtalc as nucleating agent, nanoclay as reinforcing agent, and their synergetic effect were investigated on the cell density, cell size, and expansion ratio of hybrid microtalc/nanoclay polyethylene nanocomposites. The cell density was improved by 96% and 89% by addition of 1 wt% of microtalc and nanoclay, respectively, compared to pure polyethylene foams. The cell size was reduced by 20% and 17.5% in 1 wt% of microtalc and nanoclay, respectively. However, the synergetic effect of using both microtalc and nanoclay at 1 wt% was more significant compared to their individual effects. The cell density was enhanced by 313% and the cell size was decreased by 35% compared to pure samples.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.468
Teacher spread0.392 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicPolymer Foaming and CompositesFrench-language works237,207