Multi-Stacked Supported Lipid Bilayer Micropatterning through Polymer Stencil Lift-Off
Bibliographic record
Abstract
Complex multi-lamellar structures play a critical role in biological systems, where they are present as lamellar bodies, and as part of biological assemblies that control energy transduction processes. Multi-lamellar lipid layers not only provide interesting systems for fundamental research on membrane structure and bilayer-associated polypeptides, but can also serve as components in bioinspired materials or devices. Although the ability to pattern stacked lipid bilayers at the micron scale is of importance for these purposes, limited work has been done in developing such patterning techniques. Here, we present a simple and direct approach to pattern stacked supported lipid bilayers (SLBs) using polymer stencil lift-off and the electrostatic interactions between cationic and anionic lipids. Both homogeneous and phase-segregated stacked SLB patterns were produced, demonstrating that the stacked lipid bilayers retain lateral diffusivity. We demonstrate patterned SLB stacks of up to four bilayers, where fluorescence resonance energy transfer (FRET) and quenching was used to probe the interactions between lipid bilayers. Furthermore, the study of lipid phase behaviour showed that gel phase domains align between adjacent layers. The proposed stacked SLB pattern platform provides a robust model for studying lipid behaviour with a controlled number of bilayers, and an attractive means towards building functional bioinspired materials or devices.
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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 | 0.001 |
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".