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Record W2093896718 · doi:10.1002/masy.200351227

Weak flocculation of aqueous kaolin suspensions initiating by NaCMC with different molecular weights

2003· article· en· W2093896718 on OpenAlexaff
Y. Li, Enikő Nagy, M. N. Esmail, Zoltán Hórvölgyi

Bibliographic record

VenueMacromolecular Symposia · 2003
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsConcordia University
Fundersnot available
KeywordsThixotropyFlocculationRheologyPolymerViscosityAqueous solutionReduced viscosityMaterials scienceRelative viscosityElasticity (physics)Apparent viscosityChemical engineeringChemistryChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The present work is an investigation of the effect of NaCMC with different viscosities (molecular weights) on the stability of aqueous kaolin suspensions at pH 5‐6. The stabilizing effect of polymers was characterized by measuring the sedimentation volumes (for 2.5% kaolin suspensions) and some important rheological parameters (for 40% and 50% kaolin suspensions). In certain cases the stability of suspensions was also studied in the presence of 0.5‐1.0% NaCl. The additives were incorporated into the suspensions separately and simultaneously, as well. In certain cases the effect of mixing order of NaCMCs was also studied. The lower viscosity NaCMC was found to be a better stabilizing agent than its medium viscosity counterpart at the studied polymer concentrations (0.005‐1.0%). This was manifested in smaller sedimentation volumes and lower rheological parameters (viscosity, yield stress, degree of thixotropy and elasticity). The lower and the medium viscosity polymer were simultaneously and consecutively added in a mass ratio of 50:1 and 10:1. The resulted observation of low viscosity and yield stress, and more importantly thixotropy and elasticity, can be interpreted in terms of a “site‐blocking” type flocculation.

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 score0.019
Threshold uncertainty score0.872

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 head0.007
GPT teacher head0.229
Teacher spread0.223 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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