Micropatterning of Phase-Segregated Supported Lipid Bilayers and Binary Lipid Phases through Polymer Stencil Lift-Off
Bibliographic record
Abstract
Supported lipid bilayers (SLBs) provide an excellent model system for studying structural and functional characteristics of biomembranes. Patterning model membranes on solid supports has elicited much interest because lipid bilayer arrays at cellular or subcellular scales provide attractive platforms for reconstituting tissue-like conditions for cell culture, and for creating simplified physiological environments to study biological processes. Phase-segregated SLB patterns can be especially useful for such studies, as the selective functionalization of the lipid phases with different lipids, receptors, or proteins can be achieved to mimic the key features of plasma membrane. However, it remains challenging to pattern phase-segregated lipid bilayers and to spatially control the lipid phases at the micron scale. Current methods to achieve this involve multiple surface modification and patterning steps, elaborate techniques such as microfluidic, microcontact printing, or electrochemical control, among others. To overcome the complexity in producing phase-segregated patterns, we have developed simple and rapid strategies to pattern SLBs with phase separation utilizing the polymer stencil lift-off (PSLO) technique. PSLO is a powerful technique for SLB patterning, since it allows the faithful pattern transfer of micron-sized lipid domains onto solid surfaces under aqueous conditions, which eliminates the need for controlled humidity and reduces the risk of bilayer disruption through drying. By integrating postetching substrate cleaning and a blocking treatment, well-defined homogeneous and phase-segregated SLB patterns were achieved with lipid mobility that matches that of SLBs formed on clean SiO 2 wafer substrates. A two-step incubation method was also developed for patterning binary lipid phases, which allowed precise control of their position and geometries. The created phase-segregated SLB patterns were used to study lipid phase behavior within confined areas, and quantitative analysis showed that smaller pattern sizes resulted in smaller gel phase domains, which also covered a smaller fraction of the total patterned SLB area. This was attributed to the decreased mobility of the bottom leaflet of the SLB, which lies in close proximity to the substrate, and the resulting hindered exchange of lipid molecules between the bottom and upper leaflets through the SLB boundary. By further integration with functional groups, the phase-segregated lipid bilayer patterns might find relevant application in tissue engineering, biophysical studies of biomolecular and cellular interactions, and biosensing platforms.
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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 | 0.000 |
| 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".