An Analytical Model for Estimation of the Self-Diffusion Coefficient and Adsorption Kinetics of Surfactants Using Dynamic Interfacial Tension Measurements
Bibliographic record
Abstract
We report a new analytical approach to model the transient diffusion and adsorption kinetics of a surfactant at a liquid/liquid interface using dynamic interfacial tension data. The developed model combined with the Frumkin/Langmuir isotherm is used to reproduce the experimental data of dynamic interfacial tension and predict the surfactant diffusion coefficient from a bulk solution to an interface and its adsorption kinetics. Experimental data of the dynamic interfacial tension of toluene and heptol solutions at various concentrations of asphaltenes (a natural surfactant) were employed to examine the ability of the developed model to regenerate the dynamic interfacial tension data. The model enabled us to estimate the apparent diffusion coefficient and adsorption kinetics of asphaltenes at different concentrations. The results showed that the diffusive migration of asphaltene toward an oil/water interface decreases at its higher concentrations and increases at higher concentrations of an aliphatic solvent such as n -heptane. Furthermore, the results reveal that the adsorption rate of asphaltenes at the interface increases at higher concentrations of surfactants and the aliphatic solvent. The developed analytical model finds applications in the prediction of the diffusion and adsorption kinetics of surfactants using dynamic interfacial tension data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".