Dynamic Surface Excesses of Fluorocarbon Surfactants
Bibliographic record
Abstract
Dynamic adsorption and surface tension behavior of aqueous fluoro-surfactant solutions has been investigated using a range of experimental techniques, including neutron reflection (NR). Equilibrium tensions, γ eq, have been measured by drop volume tensiometry (DVT), and dynamic surface tensions, γ dyn, have been determined using the nonperturbative method, surface light scattering (SLS). Dynamic conditions for NR and SLS measurements were established using an overflowing cylinder (OFC). The OFC provides a dynamic surface on the 0.1−1 s time scale and offers a large (∼50 cm 2 ), near flat surface for interrogating interfacial properties. To exploit these techniques effectively, a fluorocarbon anionic surfactant, sodium bis(1 H,1 H -nonafluoropentyl)-2-sulfosuccinate (di-CF4) has been specifically selected. Molecular structure effects have been explored with the C6 analogue sodium bis(1 H,1 H,7 H -dodecafluoro- n -heptyl) sulfosuccinate (di-HCF6). Using OFC−NR, dynamic surface excesses, Γ dyn, have been measured directly, and these values have been compared to equilibrium coverages, Γ eq, determined by DVT and NR. Close to the critical micelle concentration (cmc) of di-CF4 (1.58 mmol dm - 3 ), Γ dyn and Γ eq are very similar, and the ratio φ = Γ dyn /Γ eq is unity to within the precision of the experiment. For moderate differences in surface tension, up to Δγ = γ dyn − γ eq ≤ 15 mN m - 1, φ remains close to 1. At concentrations of 0.2−0.7 mmol dm - 3, the dynamic surface excess Γ dyn is measurably different from the equilibrium value Γ eq (φ < 1). This concentration range coincides with the largest differences in surface tension, Δγ. For both di-CF4 and di-HCF6, the maximum values of Δγ and ΔΓ occur around the same bulk concentration, ∼0.7 mmol dm - 3, suggesting that dynamic surface behavior is determined mainly by mass transport (which is related to the bulk concentration), rather than surfactant properties such as cmc, for these surfactants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".