MétaCan
Menu
Back to cohort
Record W4237721893 · doi:10.1515/ipp-2020-350512

A Comparison of CO<sub>2</sub> and N<sub>2</sub> Foaming Behaviors of PP in a Visualization System

2020· article· en· W4237721893 on OpenAlex
Q.-P. Guo, J. Wang, Chul B. Park

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Polymer Processing · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolypropyleneMaterials scienceNucleationFoaming agentBlowing agentComposite materialPressure dropDrop (telecommunication)Void (composites)Mole fractionChemical engineeringPorosityThermodynamicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Understanding of polypropylene (PP) foaming is critically important to reduce the weight of automotive parts. In this study, we used a batch foaming simulation system with visualization cell, to observe the foaming behaviors of PP that is blown with CO 2 and N 2 under various experimental conditions. We found that the nucleating agent content, initial temperature, pressure (i. e., gas content), and pressure drop rate during foaming have a significant effect on cell nucleation and cell growth. The cell density and the void fraction of PP foamed with CO 2 and N 2 , respectively, were separately observed and compared. It was found that under the same experimental conditions, the maximum cell density of PP foamed with CO 2 was higher than that of PP foamed with N 2 . However, the maximum cell density of PP foamed with CO 2 was determined to be lower than that of PP foamed with N 2 , when the same gas mole numbers were employed. Based on the experimental results, optimum foaming conditions and effective processing strategies for PP-CO 2 system are suggested.

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.

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 score0.020
Threshold uncertainty score0.725

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 head0.020
GPT teacher head0.306
Teacher spread0.285 · 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