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Record W4315476332 · doi:10.1177/0021955x221150863

Autoclave foaming and steam-chest molding of polypropylene/polybutene-1 blend bead foams and their crystallization and mechanical properties

2023· article· en· W4315476332 on OpenAlexaff
Xiaoqin Lan, Pengke Huang, Yunkai Chong, Fei Wu, Yaozhuo Su, Haibin Luo, Partrick C. Lee, Wenge Zheng

Bibliographic record

VenueJournal of Cellular Plastics · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceAutoclavePolypropyleneMolding (decorative)Composite materialThermoformingCrystallizationBeadFoaming agentBlow moldingChemical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Expanded polypropylene (EPP) foams have showed wide applications in our daily life, such as automotive and packaging. Usually, autoclave foaming combined with steam-chest molding is the main artwork to prepare the high-precision EPP foam products. However, the foaming behavior of EPP and the excessive pressure required for molding still need to be further improved, which is great significance for energy saving and cost saving, etc. Herein, this study finds that adding a certain amount of polybutene-1 (PB-1) into the PP can help to reduce the temperature and pressure required for foaming/molding, and to broaden the foaming temperature. For example, in order to make the foam beads bonding well and with the expansion ratio of 20, the molding pressure should be higher than 2.7 bar for Neat PP foams, but just 1.5 bar for PP/PB-1 mixtures. Moreover, the effects of PB-1 content on the crystallization properties and foaming/molding behaviors of the PP/PB-1 bead foams are illustrated, and then the mechanical properties are also studied. Furthermore, the low-pressure foaming strategy presented here is beneficial for reducing the barriers of energy consumption and promoting the development of new bead foam materials.

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 score0.018
Threshold uncertainty score0.439

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 head0.018
GPT teacher head0.202
Teacher spread0.184 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

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