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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.007
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)0.0030.000

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