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Record W4292440713 · doi:10.1002/pen.26117

Physical aging of <scp>PC</scp>:<scp>PS</scp> blends: Dynamic mechanical analysis and nuclear magnetic resonance studies

2022· article· en· W4292440713 on OpenAlexaff
Imane Charif, Naima Doulache, A. Gourari, Denis Rodrigue, Yann Giroux, Rabah Cherfi

Bibliographic record

VenuePolymer Engineering and Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials sciencePolycarbonateDynamic mechanical analysisGlass transitionPolystyreneViscoelasticityFourier transform infrared spectroscopyChemical engineeringPolymerComposite material

Abstract

fetched live from OpenAlex

Abstract The effect of physical aging of the partially miscible blend of polycarbonate:polystyrene (PC:PS) at temperatures near the glass transition temperature ( T g ) of the PS was studied as a function of time. For this purpose, blends of PC and PS with different ratios were elaborated and characterized using SEM, FTIR, DSC, and DMA techniques. The results indicated the presence of weak interaction upon blending with a maximum of interaction for the 50:50 blend. The effect of physical aging on the latter was then investigated via DMA, DSC, and NMR analyses. The DMA results showed that both phases were sensitive to aging. The effect was found to be strongly dependent on both the temperature and time. The aging of PC:PS blend was also found to trap stress during the process which induced an instability in the viscoelastic behavior. The latter was found to be associated with a phase‐separation morphology of the blend and influenced by its composition. The NMR analysis also showed a pronounced distortion of the conformation combined with increasing molecular motion. These effects were found to be important for aging below the PS's T g .

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)0.001
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 score0.445
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.228
Teacher spread0.218 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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