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Record W1974351545 · doi:10.14356/kona.2000012

Effect of Surfactant and Polymer Adsorption on the Viscosity of Aqueous Colloidal Silica Dispersions under Extreme Conditions

2000· article· en· W1974351545 on OpenAlexaff
Abbas A. Zaman, Pankaj Kumar Singh, Brij M. Moudgil

Bibliographic record

VenueKONA Powder and Particle Journal · 2000
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsInstitute of Particle Physics
FundersUniversity of FloridaNational Science Foundation
KeywordsPulmonary surfactantMaterials sciencePolymerViscosityColloidChemical engineeringDispersion (optics)Dispersion stabilityAqueous solutionCationic polymerizationDispersantAdsorptionRheologyReduced viscosityIonic strengthPolymer chemistryComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of surfactant and polymeric additives on the viscosity behavior and stability of aqueous colloidal dispersions of silica particles under extreme conditions (low pH, high ionic strength) has been investigated. The surfactant and polymer used as dispersing agent were C12TAB, a cationic surfactant, and DarvanC, a commercially available polymer. It was found that the surfactant stabilized dispersions show a lower viscosity and a more uniform resistance to flow than the samples stabilized through electrostatic repulsion or polymer induced forces in the system. Stability analysis through turbidity measurements indicated that the state of the dispersion changes from an unstable regime to a stable regime above a critical concentration of C12TAB in the system. Viscosity measurements as a function of temperature indicates that C12TAB dispersing agent can further improve the flowability of the dispersion at higher temperatures.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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 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

Citations4
Published2000
Admission routes1
Has abstractyes

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