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Record W2090659600 · doi:10.1021/jp201972n

Phase Behavior of Rod–Coil Diblock Copolymer and Homopolymer Blends from Self-Consistent Field Theory

2011· article· en· W2090659600 on OpenAlexaff
Wendi Song, Ping Tang, Feng Qiu, Yuliang Yang, An‐Chang Shi

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2011
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceCopolymerElectromagnetic coilPhase (matter)MonolayerBilayerPhase diagramChemical physicsPolymer chemistryComposite materialPolymerMembraneNanotechnologyChemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The phase behavior of binary blends of rod-coil diblock copolymers and coil or rod homopolymers is studied by the self-consistent field theory (SCFT). The rod blocks are modeled as wormlike chains and the corresponding SCFT equations are solved using a hybrid method, in which the orientation-dependent functions are discretized on a unit sphere, while the positional space-dependent functions are treated using a spectral method. Phase diagrams of the blends are constructed as a function of the homopolymer volume fraction and phase segregation strength. It is discovered that the phase behavior of the system depends on the flexibility of the homopolymers. The addition of coil-homopolymers stabilizes the smectic phases. Low-molecular weight coil-homopolymers tend to mix with the coil-blocks, whereas high-molecular weight coil-homopolymers are mostly localized at the center of the coil-domains. On the other hand, the addition of rod-homopolymers strongly affects the orientation ordering of the system, leading to transitions between monolayer smectic-C, monolayer smectic-A and bilayer smectic-A phases.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations31
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicBlock Copolymer Self-AssemblyFrench-language works237,207