Surface miscibility of <scp>Gemini</scp> surfactants and <scp>DOPE</scp> in binary mixed monolayers
Bibliographic record
Abstract
Abstract Surface pressure (π)–molecular area (A) isotherms were gathered to characterize the packing of binary mixed Langmuir monolayers of 1,2‐dioleoyl‐sn‐glycero‐3‐phosphoethanolamine (DOPE) and one of two Gemini surfactants (GS), N,N‐bis(dimethyloctadecyl)‐1,7‐nonanediammonium dibromide (18‐7‐18) or 1,9‐bis(octadecyl)‐1,1,9,9‐tetramethyl‐5‐amino‐1,9‐nonanediammonium dibromide (18‐7NH‐18) of varying molar fractions. Information about miscibility behavior was derived from the π–A curves by examining the excess free energy of mixing (ΔGexc) that was calculated through the surface area additivity rule. Surface compressibility modulus (Cs−1) was also used to characterize intermolecular interactions. Mutual interactions between GS and DOPE were analyzed in terms of excess Gibbs energy of mixing and the value of this parameter depended strongly on the composition of the mixed film. GS and DOPE are generally miscible as DOPE reduces intermolecular repulsion between highly charged GS molecules leading to the formation of more densely packed mixed monolayers. However, GS and DOPE are immiscible in equimolar mixtures due to tail group packing mismatch between saturated and unsaturated alkyl chains. The prevalence of attractive synergistic interactions in the monolayers studied differs from a previous finding of antagonistic mixing behavior in GS/DOPE micelles. These results contribute to the understanding of GS‐lipid interactions and packing that are critical to the in vitro and in vivo stability of liposomes composed of these molecules used for non‐viral gene therapy applications.
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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) | 0.000 | 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".