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Record W2517637017 · doi:10.1177/026248930302200201

Foaming Polystyrene with Mixtures of Carbon Dioxide and HFC-134a

2003· article· en· W2517637017 on OpenAlex

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

VenueCellular Polymers · 2003
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBlowing agentMaterials sciencePolystyreneNucleationPlasticizerSolubilityMolar massRheologyCarbon dioxideChemical engineeringGaseous diffusionThermodynamicsPolymerOrganic chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

Mixtures of blowing agents are becoming widely used in the industry either for economical reasons or for achieving better control of processing conditions. Despite the fact that they are commonly used for foaming, the literature is fairly scarce on that particular subject and the fundamentals are not very well understood. This work studies the effect of blending carbon dioxide and 1,1,1,2-tetrafluoroethane (HFC-134a) in polystyrene. Ultrasonic monitoring and online rheology provided information on the solubility and plasticizing effect of the gases. Results show that, on an equivalent molar basis, HFC-134a is slightly more soluble than CO 2 and is a more effective plasticizer. Moreover, HFC-134a generates foam samples with a higher nucleation density than CO 2 using similar processing conditions. Blending the two gases generates nucleate cell densities, which are intermediate to the pure gases but do not follow a log-additivity rule. It is hypothesized that blending gases affect their mutual diffusion coefficients, which in turn, largely dictates the final foam morphology.

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 score0.029
Threshold uncertainty score0.617

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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