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Record W1851151605 · doi:10.1002/cjce.22243

Viscosity Modulation by Magnetic Field for Colloidal Polyelectrolyte Brushes

2015· article· en· W1851151605 on OpenAlexvenueno aff
Yicun Wen, Rui Zhang, Qingsong Li, Kaimin Chen, Li Li, Xuhong Guo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsPolyelectrolyteViscosityRheologyMaterials scienceColloidEmulsionEmulsion polymerizationAqueous solutionMagnetic fieldBrushChemical engineeringPolymerizationNanotechnologyPolymer chemistryChemistryComposite materialPolymerOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Aqueous dispersions of magnetic spherical polyelectrolyte brushes (MSPB) are prepared by mini‐emulsion followed by photo‐emulsion polymerization. Their viscosity increases dramatically upon increasing the intensity of an external magnetic field, showing the “magnetoviscous effect.” Unlike general magnetic nano‐particles, the increase in viscosity of MSPB cannot be described by Shliomis's theory, probably due to the covering of a spherical polyelectrolyte brush layer. MSPB rheology is also tunable by pH, and the brushes can stay in atmospheric conditions for months without changes in particle size and size distribution, which should make them ideal candidates for a broad range of industrial applications. The future applications of our multifunctional magnetic responsive system are promising.

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 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 score0.006
Threshold uncertainty score0.279

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)0.0000.000

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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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