Surface equation of state for pulmonary surfactant monolayers at Air–Water interface: Protein–lipid binary mixture monolayers
Bibliographic record
Abstract
Abstract The available surface equation of state for pure pulmonary surfactant monolayers is generalised to binary mixture monolayers by introducing a group of parameters, βi(i = 1–4) in the form of $x_{r}^{\beta _{i} } $ to express the influence of the components in new mixing rules and a new factor, I $\left[ { = \left( {\prod\limits_{i = 1}^{4} {\beta _{i} } } \right)^{{\raise0.5ex\hbox{$\scriptstyle 1$}\kern-0.1em/\kern-0.15em\lower0.25ex\hbox{$\scriptstyle {4}$}}} } \right]$ , is defined to represent the interaction intensity between two different components. The π − A isotherms getting by the surface equations of state agree with the experimental data for protein–lipid binary monolayers, and the average deviation is about 11.41%. The result shows the order of the interaction intensity between protein and lipid is SP‐C/DPPG > SP‐B/DPPG > SP‐C/DPPC > SP‐B/DPPC. L'équation d'état de la surface disponible pour les monocouches de surfactant pulmonaire pures est généralisée en monocouches de mélange binaire en introduisant un groupe de paramètres, βi(i = 1–4) dans une forme qui permet d'exprimer l'influence des composantes dans de nouvelles règles de mélange et un nouveau facteur, I $\left[ { = \left( {\prod\limits_{i = 1}^{4} {\beta _{i} } } \right)^{{\raise0.5ex\hbox{$\scriptstyle 1$}\kern-0.1em/\kern-0.15em\lower0.25ex\hbox{$\scriptstyle {4}$}}} } \right]$ , est défini pour représenter l'intensité de l'interaction entre deux composantes différentes. Les isothermes π − A des équations d'état de la surface concordent avec les données expérimentales pour les monocouches binaires protéines‐lipides et la déviation moyenne est d'environ 11,41%. Le résultat démontre que l'ordre de l'intensité de l'interaction entre les protéines et les lipides est SP‐C/DPPG > SP‐B/DPPG > SP‐C/DPPC > SP‐B/DPPC. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".