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Record W4200082823 · doi:10.3390/polym14010044

Tuning High and Low Temperature Foaming Behavior of Linear and Long-Chain Branched Polypropylene via Partial and Complete Melting

2021· article· en· W4200082823 on OpenAlexaff
Mu Sung Kweon, Mahmoud Embabi, Maksim E. Shivokhin, Anvit Gupta, Xuejia Yan, George J. Pehlert, Patrick Lee

Bibliographic record

VenuePolymers · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersConsiglio Nazionale delle RicercheExxon Mobil Corporation
KeywordsMaterials scienceExpansion ratioSupercoolingPolypropyleneCrystallizationMelting pointMelting temperatureComposite materialExtensional definitionDrop (telecommunication)Blowing agentHardening (computing)ThermodynamicsMechanical engineering

Abstract

fetched live from OpenAlex

While existing foam studies have identified processing parameters, such as high-pressure drop rate, and engineering measures, such as high melt strength, as key factors for improving foamability, there is a conspicuous absence of studies that directly relate foamability to material properties obtained from fundamental characterization. To bridge this gap, this work presents batch foaming studies on one linear and two long-chain branched polypropylene (PP) resins to investigate how foamability is affected by partial melting (Method 1) and complete melting followed by undercooling (Method 2). At temperatures above the melting point, similar expansion was obtained using both foaming procedures within each resin, while the PP with the highest strain hardening ratio (13) exhibited the highest expansion ratio (45 ± 3). At low temperatures, the foamability of all resins was dramatically improved using Method 2 compared to Method 1, due to access to lower foaming temperatures (<150 °C) near the crystallization onset. Furthermore, Method 2 resulted in a more uniform cellular structure over a wider temperature range (120-170 °C compared to 155-175 °C). Overall, strong extensional hardening and low onset of crystallization were shown to give rise to foamability at high and low temperatures, respectively, suggesting that both characteristics can be appropriately used to tune the foamability of PP in industrial foaming applications.

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 score0.011
Threshold uncertainty score0.731

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 head0.010
GPT teacher head0.229
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

Citations15
Published2021
Admission routes1
Has abstractyes

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