MétaCan
Menu
Back to cohort
Record W2080406735 · doi:10.1139/v04-157

Effects of di-, tri-, and tetraethylene glycols on the thermodynamic and micellar properties of Triton X-100 in water

2004· article· en· W2080406735 on OpenAlexvenueno aff
Gwen MacIsaac, Aiysha Al-Wardian, Karen M. Glenn, R. Palepu

Bibliographic record

VenueCanadian Journal of Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMicelleSolventSurface tensionPyreneAggregation numberPulmonary surfactantGibbs isothermMoleculeHydrogen bondEthylene glycolQuenching (fluorescence)Solvent effectsFluorescenceCritical micelle concentrationPhysical chemistryThermodynamicsOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Micellar and surface thermodynamic properties of aggregation of Triton X-100 (TX-100) in mixed solvent systems containing di-, tri-, and tetraethylene glycol with water have been investigated by employing surface tension, density, and fluorescence methods. The differences in Gibbs energies of micellization between water and binary solvent mixtures were determined to evaluate the influence of the co-solvent on the micellization process. From the surface tension measurements, the effects of the co-solvent on parameters such as surface excess, minimum area per molecule, and surface pressure indicate that the surface activity of the surfactant decreases with increasing concentration of the glycol co-solvent. Partial specific volumes, obtained from density measurements, indicate that the fraction of solvent molecules interacting with the micelles by hydrogen bonding vary with the type of additive. Fluorescence studies reveal that quenching of the pyrene probe by cetyl pyridinum chloride in TX-100 micelles is accompanied by simultaneous static and dynamic processes.Key words: Triton X-100, glycols, thermodynamics, micelles, fluorescence quenching.

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 score0.004
Threshold uncertainty score0.317

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 head0.006
GPT teacher head0.166
Teacher spread0.160 · 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

Citations22
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicSurfactants and Colloidal SystemsFrench-language works237,207