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Record W1996824698 · doi:10.1002/app.32697

A new crystallization kinetics study of polycarbonate under high‐pressure carbon dioxide and various crystallinization temperatures by using magnetic suspension balance

2010· article· en· W1996824698 on OpenAlexaff
Li Guo, Chul B. Park

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrystallizationCrystallinityPolycarbonateMaterials scienceChemical engineeringSaturation (graph theory)Suspension (topology)Isothermal processThermodynamicsPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract A new approach for researching the effects of induced crystallization of polycarbonate (PC) by pressurized supercritical CO2 using a magnetic suspension balance is described herein. Our study systematically investigated the effects of saturation temperature and pressure on crystallization kinetics and thermal behavior of crystallized PC. It was observed that either increasing the saturation pressure of CO2 or the crystallization temperature was effective in promoting the mobility of PC's molecular chains. Thermal behavior and crystallization rate were affected in the following manner: a higher PC molecular chain mobility increased the degree of crystallinity, the melting temperature, and the crystal growth rate. However, the crystal growth dimension changed from a three‐dimensional to a one‐dimensional configuration as the isothermal crystallization temperature was raised incrementally from 140°C to 160°C to 180°C, resulting in an overall decrease in the crystallization rate. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.219
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2010
Admission routes1
Has abstractyes

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