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Record W4233956437 · doi:10.32920/ryerson.14652258

A computational study of phase separation in polymer solutions under a double quench

2021· preprint· en· W4233956437 on OpenAlexaff
Ehsan Hosseini

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSpinodal decompositionSpinodalPhase diagramNucleationMaterials scienceThermodynamicsMetastabilityQuenching (fluorescence)PolymerPhase (matter)Mixing (physics)Binary numberWork (physics)Chemical physicsStatistical physicsPhysicsMathematicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Polymer-dispersed liquid crystals (PDLCs) are a relatively new class of materials used for many applications ranging from switchable windows to projection displays. PDLSs are formed by spinodal decomposition induced by thermal quenching or polymerization. The objective of the present study is to introduce a new mechanism of phase separation in a binary polymer solution and develop a mathematical model and computer simulation to describe the phase separation during the early and intermediate stages of nucleation and growth and spinodal decomposition induced by thermal double quenching. The growth equilibrium limits of phase separation as well as phase transition are calculated by taking into consideration the Flory-Huggins theory for the free energy of mixing. A two step quench is modeled using Cahn-Hilliard theory for asymmetric binary polymer solution which is quenched from a stable state in the one-phase region to a metastable region where nucleation and growth occurs. The solution is allowed to coarsen for different time periods before a second quench was applied to a point further inside the phase diagram. The numerical results in two dimensions replicate the experimental and numerical work that has been recently done and published.

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 categoriesInsufficient payload (model declined to judge)
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 score0.231
Threshold uncertainty score0.999

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.436
Teacher spread0.333 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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