Adsorption Kinetics of Aqueous n-Alcohols: A New Kinetic Equation for Surfactant Transfer
Bibliographic record
Abstract
The surface tension and adsorption kinetics of aqueous solutions of slightly volatile, organic amphiphiles are influenced by both liquid- and vapor-phase surfactant concentrations. Here we derive a new kinetic transfer equation, based on the classic Langmuir analysis, which can account for adsorption and desorption from both sides of the vapor/liquid interface during surface equilibration. The new transfer equation was tested against dynamic surface tension data from two normal alcohols (1-butanol and 1-hexanol) in aqueous solutions. The experimental data was collected at conditions where the dynamic surface tension is controlled by a combination liquid- and vapor-phase surfactant adsorption. The validity of the transfer equation was assessed based on its ability to model the experimental data accurately and generate suitable values for the kinetic rate constants. The theoretical predictions from the transfer equation fit well with the experimental data for both systems. However, variability was observed in the least-squares estimates of the rate constants. The variability is attributed to the limitations of empirical models that utilize adjustable fitting parameters to optimize the model predictions and the wide range of surfactant concentrations studied. Specific concentration regions were identified where the variability in the rate constants was minimal and, thus, where the model is most appropriate. The new transfer equation can be applied to volatile surfactant systems where the dynamic surface tension is influenced by surfactant adsorption and desorption from both sides of the vapor/liquid interface.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".