Redox-Induced Lipid Vesicle Fusion Onto Electroactive Self-Assembled Monolayers
Bibliographic record
Abstract
Supported lipid bilayers (SLBs) are popular model systems to study cell membrane functionalities and various biomolecular interaction forces. A key advantage of SLBs on metallic surfaces, is the application of spectroscopic, electrochemical, and surface plasmon resonance techniques for biomolecular recognition studies. The most common method for forming SLBs is vesicle fusion, which involves the adsorption, deformation, and rupture of small unilamellar vesicles (SUVs) of lipids from aqueous suspension to the substrate surface.1 The formation of continuous single bilayers by vesicle fusion can be problematic due to the many experimental parameters influencing vesicle rupture and bilayer spreading (i.e., lipid and vesicle properties, physicochemical characteristics of the surface, temperature, and solvent environment)1. Vesicle fusion typically works only with smooth hydrophilic surfaces, such as glass, silica, and mica. Although approaches have been developed to form bilayers on technologically relevant surfaces such gold (e.g., surface functionalization with hydrophilic organic films,2 solvent-assisted lipid bilayer formation3 or the addition of a vesicle-destabilizing agent4), there remains a need for active strategies that are fast, versatile, and scalable. We present a redox-induced approach for the formation of single bilayers on gold functionalized with electroactive self-assembled monolayers (SAMs) of ferrocenylalkanethiolates. The electrochemical oxidation of the SAM-bound ferrocene (Fc) to ferrocenium (Fc+) involves coupled electron transfer and ion pairing reactions. Counteranions from solution pair with the ferroceniums to stabilize the oxidized cations and neutralize the excess positive charge at the SAM/aqueous interface. The surface-confined redox reaction triggers the formation of single bilayer membranes from SUVs of anionic or zwitterionic phospholipids onto gold surfaces modified with the electroactive SAM. The ion pairing association of the charged lipid head groups with the electrogenerated ferroceniums drives the assembly of the phospholipids on the SAM surface to produce solid-supported bilayers of high surface coverage (≳ 90%) from gel- or fluid-phase forming phospholipids within minutes at room temperature. The redox-mediated strategy reported in this work is a conceptual advance in the preparation of solid-supported lipid bilayers. It is fast, insensitive to the phase state of the phospholipid in the vesicle precursor, and not limited to hydrophilic surfaces. References Richter, R. P.; Bérat, R.; Brisson, A. R., Formation of Solid-Supported Lipid Bilayers: An Integrated View. Langmuir 2006, 22 (8), 3497-3505. Silin, V. I.; Wider, H.; Woodward, J. T.; Valincius, G.; Offenhausser, A.; Plant, A. L., The Role of Surface Energy on the Formation of Hybrid Bilayer Membranes. J. Am. Chem. Soc. 2002, 124, 14676-14683. Ferhan, A. R.; Yoon, B. K.; Park, S.; Sut, T. N.; Chin, H.; Park, J. H.; Jackman, J. A.; Cho, N.-J., Solvent-Assisted Preparation of Supported Lipid Bilayers. Nat. Protoc. 2019, 14 (7), 2091-2118. Cho, N.-J.; Cho, S.-J.; Cheong, K. H.; Glenn, J. S.; Frank, C. W., Employing an Amphipathic Viral Peptide to Create a Lipid Bilayer on Au and TiO2. J. Am. Chem. Soc. 2007, 129 (33), 10050-10051. Figure 1
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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) | 0.001 | 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".