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Record W4414163210 · doi:10.1063/5.0284817

Diffusion-adsorption kinetics of anionic surfactants at a liquid–liquid interface: An analytical and experimental study

2025· article· en· W4414163210 on OpenAlexafffund
Shams Kalam, Hassan Hassanzadeh

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDodecylbenzeneAdsorptionSurface tensionThermal diffusivityPulmonary surfactantDiffusionKineticsSodium dodecyl sulfateGibbs isotherm

Abstract

fetched live from OpenAlex

We present a new dynamic interfacial adsorption model for estimating the diffusion and adsorption kinetics of the anionic surfactants sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS) at an n-decane–water interface. The migration of surfactant molecules to the interface progressively reduces the dynamic interfacial tension. From these data, equilibrium fitting parameters are estimated using adsorption models, including the Langmuir, Frumkin, van der Waals, and Volmer models. The experimental data are further analyzed using the proposed analytical model, which calculates the surface concentration as a function of diffusivity and adsorption rate. This time-dependent surface concentration is then related to interfacial tension by the Frumkin equation of state. Consequently, the apparent diffusion coefficient and adsorption rate constant are estimated from the best fit of the model to the experimentally measured data. The results show that SDS exhibits higher initial diffusivity and faster adsorption rates. In contrast, SDBS achieves a lower interfacial tension at equilibrium despite its lower diffusivity and slower adsorption rates. The comparative observations span concentrations from dilute to near the critical micelle concentration at 25 °C.

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 score0.005
Threshold uncertainty score0.761

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 head0.018
GPT teacher head0.299
Teacher spread0.281 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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