Mixed DPPC/DPPG Monolayers at Very High Film Compression
Bibliographic record
Abstract
A drop shape technique using a constrained sessile drop constellation (ADSA-CSD) has been introduced as a superior technique for studying spread films specially at high collapse pressures [Saad et al. Langmuir 2008, 24, 10843-10850]. It has been shown that ADSA-CSD has certain advantages including the need only for small quantities of liquid and insoluble surfactants, the ability to measure very low surface tension values, easier deposition procedure, and leak-proof design. Here, this technique was applied to investigate mixed DPPC/DPPG monolayers to characterize the role of such molecules in maintaining stable film properties and surface activity of lung surfactant preparations. Results of compression isotherms were obtained for different DPPC/DPPG mixture ratios: 90/10, 80/20, 70/30, 60/40, and 50/50 in addition to pure DPPC and pure DPPG at room temperature of 24 degrees C. The ultimate collapse pressure of DPPC/DPPG mixtures was found to be 70.5 mJ/m2 (similar to pure DPPC) for the cases of low DPPG content (up to 20%). Increasing the DPPG content in the mixture (up to 40%) caused a slight decrease in the ultimate collapse pressure. However, further increase of DPPG in the mixture (50% or more) caused a sharp decrease in the ultimate collapse pressure to a value of 59.9 mJ/m2 (similar to pure DPPG). The change in film elasticity was also tracked for the range of mixture ratios studied. The physical reasons for such changes and the interaction between DPPC and DPPG molecules are discussed. The results also show a change in the film hysteresis upon successive compression and expansion cycles for different mixture ratios.
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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 | 0.000 |
| 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 | 0.001 |
| 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".