Encapsulated Interface Bilayers for Durable Biomolecular Materials
Bibliographic record
Abstract
This paper introduces the concept of hydrogel encapsulated interface bilayers as a novel approach for creating durable encapsulated biomolecular materials. The regulated attachment method (RAM) is used to form encapsulated interface bilayers from lipid-encased aqueous volumes contained in a deformable supporting substrate. Physically-encapsulated interface bilayers exhibit increased durability and portability over droplet interface bilayedrs and RAM enables the in situ bilayer formation without the need to dispense and arrange individual droplets. The results presented in this paper demonstrate that poly(ethylene glycol) dimethacrylate monomers (PEG-DMA, Mw = 1000), a photopolymerizable hydrogel monomer, and Irgacure 2959 photoinitiator can be incorporated into the aqueous phase in order to form hydrogel encapsulated interface bilayers. Following bilayer formation, exposure to an ultraviolet (UV) light initiates photopolymerization of the polymer on both sides of the bilayer, creating interface bilayers between solid aqueous phases. Electrical recordings of bilayer formation in the liquid state confirm that interface bilayers formed from photopolymerizable aqueous solutions have both high electrical resistances > 1GΩ necessary for observing transmembrane protein gating and survive the UV curing procedure required to polymerize the hydrogel. Photopolymerization for 60 seconds using a 1W hand held UV spot cure light produced water-swollen solids on both sides of the membrane. Hydrogel encapsulated interface bilayers last for hours to days and retain the fluidity necessary for delivering alamethicin proteins to the interface.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".