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Record W2329668000 · doi:10.1021/la1040932

Effect of Temperature on the Surface Tension of 1-Hexanol Aqueous Solutions

2011· article· en· W2329668000 on OpenAlexaff
Abdolhamid Firooz, P Chen

Bibliographic record

VenueLangmuir · 2011
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSurface tensionAdsorptionThermodynamicsChemistryGibbs isothermAqueous solutionPhase (matter)Reaction rate constantLangmuir adsorption modelLangmuirSteady state (chemistry)KineticsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This paper describes the effect of temperature on the surface tension and adsorption kinetics of 1-hexanol aqueous solutions. The experiments were performed in a closed chamber where both liquid and vapor phases coexisted, and the surface tension was influenced by a combination of liquid and vapor phase adsorption. The surface tension of 1-hexanol aqueous solutions at steady-state was found to decrease upon an increase in temperature, and a linear relationship was observed between them. The modified Langmuir equation of state and the modified kinetic transfer equation were used to model the experimental data of the steady-state and dynamic (time-dependent) surface tension, respectively. The equilibrium constants and adsorption rate constants were evaluated through nonlinear regression for temperatures ranging from 10 to 35 °C. From the steady-state modeling, the equilibrium constants for adsorption from vapor phase and liquid phase were found to increase with temperature. From the dynamic modeling, the adsorption rate constants for adsorption from vapor phase and liquid phase were found to increase with temperature too. Small deviations from the experimental data have been observed in the dynamic modeling. These deviations may be due to the experimental errors or more likely the limitations of the model used.

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 score0.035
Threshold uncertainty score0.502

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 head0.017
GPT teacher head0.215
Teacher spread0.197 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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