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Structure and viscoelasticity of interacting spherical brushes

2000· article· en· W2020937933 on OpenAlexaff
Dimitris Vlassopoulos, M. Kapnistos, George Fytas, Jacques Roovers

Bibliographic record

VenueMacromolecular Symposia · 2000
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsViscoelasticityColloidPolymerSPHERESChemical physicsMaterials scienceHysteresisDiffusionRelaxation (psychology)SwellingShear (geology)ThermodynamicsChemistryComposite materialCondensed matter physicsPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Star polymers with a high functionality of 128 are viewed as ideal spherical brushes with soft colloidal character, encompassing both polymer-like short range and colloid-like long range interactions. We have identified three mechanisms for relaxing the concentration and density fluctuations in semidilute solution: cooperative diffusion, characteristic of polymers, and self-diffusion and structural relaxation, characteristics of colloidal hard spheres. Upon heating these systems undergo a reversible liquid-solid transition, yielding weak gels. It is attributed to the trapping of the spheres in “cages” due to the swelling with temperature and hence stronger arm interpenetration. At the transition strong hysteresis phenomena are observed during steady and transient shear measurements, and relate to the changing viscoelastic character of the fluid with temperature.

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 score0.002
Threshold uncertainty score0.998

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)0.0020.000

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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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

Citations2
Published2000
Admission routes1
Has abstractyes

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