Potential Controls the Interaction of Liposomes with Octadecanol-Modified Au Electrodes: An in Situ AFM Study
Bibliographic record
Abstract
The formation of supported lipid bilayers using liposomes requires interaction with the solid surface, rupture of the liposome, and spreading to cover the surface with a lipid bilayer. This can result in a less-than-uniform coating of the solid surface. Presented is a method that uses the electrochemical poration of an adsorbed lipid-like layer on a Au electrode to control the interaction of 100 nm DOPC liposomes. An octadecanol-coated Au-on-mica surface was imaged using tapping-mode AFM during the application of potential in the presence or absence of liposomes. When the substrate potential was made negative enough, defects formed in the adsorbed layer and new taller features were observed. More features were observed and existing features increased in size with time spent at this negative poration potential. The new features were 1.8-2.0 nm higher than the octadecanol-coated gold surface, half the thickness of a DOPC bilayer. These features were not observed in the absence of liposomes when undergoing the same potential perturbation. In the presence of liposomes, the application of a poration potential was needed to initiate the formation of these taller features. Once the applied potential was removed, the features stopped growing and no new regions were observed. The size of these new regions was consistent with the footprint of a flattened 100 nm liposome. It is speculated that the DOPC liposomes were able to interact with the defects and became soluble in the octadecanol, creating a taller region that was limited in size to the liposome that adsorbed and became incorporated. This AFM study confirms previous in situ fluorescence measurements of the same system and illustrates the use of a potential perturbation to control the formation of these regions of increased DOPC content.
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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) | 0.000 | 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 | 0.001 |
| 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".