Diffusion-adsorption kinetics of anionic surfactants at a liquid–liquid interface: An analytical and experimental study
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".