Equilibrium of Adsorption of Mixed Milk Protein/Surfactant Solutions at the Water/Air Interface
Bibliographic record
Abstract
Ellipsometry and surface profile analysis tensiometry were used to study and compare the adsorption behavior of beta-lactoglobulin (BLG)/C10DMPO, beta-casein (BCS)/C10DMPO and BCS/C12DMPO mixtures at the air/solution interface. The adsorption from protein/surfactant mixed solutions is of competitive nature. The obtained adsorption isotherms suggest a gradual replacement of the protein molecules at the interface with increasing surfactant concentration for all studied mixed systems. The thickness, refractive index, and the adsorbed amount of the respective adsorption layers, determined by ellipsometry, decrease monotonically and reach values close to those for a surface covered only by surfactant molecules, indicating the absence of proteins from a certain surfactant concentration on. These results correlate with the surface tension data. A continuous increase of adsorption layer thickness was observed up to this concentration, caused by the desorption of segments of the protein and transforming the thin surface layer into a rather diffuse and thick one. Replacement and structural changes of the protein molecules are discussed in terms of protein structure and surface activity of surfactant molecules. Theoretical models derived recently were used for the quantitative description of the equilibrium state of the mixed surface layers.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".