Another Possible Rationale for Foam Stability: The Quantity and Strength of Hydrogen Bonds at the Gas–Liquid Interface
Bibliographic record
Abstract
This study examines the foams generated by three surfactants: sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), and sodium lauryl polyoxyethylene ether sulfate (AES). By analyzing the hydrogen bond at the gas-liquid interface, the research provides novel insights into the mechanisms by which surfactants stabilize foams. Surfactants adsorb at the gas-liquid interface, establishing hydrogen bonds with water molecules while simultaneously retarding the structural relaxation of water-water hydrogen bonds within the hydration layer. This phenomenon can impede drainage during the surface tension drainage phase. Surfactants that readily form hydrogen bonds with water are more likely to adsorb at the gas-liquid interface, thereby enhancing the foam stability. The presence of robust hydrogen bonds and the frequent reconstruction of these bonds contribute to the establishment of a stable hydrogen bond network, which can reinforce the gas-liquid film and potentially augment its elasticity, enabling it to better withstand external perturbations. Although the Marangoni effect typically promotes bubble coalescence, a stable hydrogen bond network may mitigate this process.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".