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Record W2102340300 · doi:10.1139/v00-124

A study of micellization and the thermodynamic properties of a series of aqueous sodium cyclohexyl alkanoate surfactants

2000· article· en· W2102340300 on OpenAlexvenueno aff
Judith A. MacInnis, D. Gerrard Marangoni, R. Palepu

Bibliographic record

VenueCanadian Journal of Chemistry · 2000
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryThermodynamics of micellizationSurface tensionAqueous solutionCritical micelle concentrationAdsorptionSpeed of soundThermodynamicsGibbs isothermMoleculeViscosityInfrared spectroscopyPhysical chemistryMicelleOrganic chemistry

Abstract

fetched live from OpenAlex

The micellization and the thermodynamic properties of a series of sodium cyclohexyl alkanoates of the general formula C 6 H 11 (CH 2 ) n COONa (where n = 1, 2, 3, 4) were investigated employing conductivity, density, surface tension, viscosity, speed of sound, luminescence probing experiments, and Fourier transform infrared (FT-IR) spectroscopy. The critical micelle concentrations (cmcs) and the aggregation numbers indicate that these surfactants have high cmc values and low aggregation numbers when compared to other single-headed surfactants (most notably the sodium alkanoates). Thermodynamic properties, obtained from the mass action model, indicate that micellization is spontaneous and entropically driven. The saturation area per molecule, the free energies of adsorption, and the efficiency and effectiveness of adsorption were determined through surface tension measurements. The presence of the cylcohexyl ring appears to influence the surface properties of micellization. Both the effectiveness and the efficiency of these surfactants, in lowering the surface tension of water, are lower than that of the straight chain alkanoates.Key words: thermodynamics, micellization, aggregation numbers, speed of sound, and spectroscopy.

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 score0.008
Threshold uncertainty score0.826

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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

Citations4
Published2000
Admission routes1
Has abstractyes

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