Lipid Bilayers as Surface Functionalizations for Planar and Nanoparticle Biosensors
Bibliographic record
Abstract
Many biological processes, pathogens, and pharmaceuticals act upon, cellular membranes. Accordingly, cell membrane mimics are attractive targets for biosensing, with research, pathology, and pharmacology applications. Lipid bilayers represent a versatile sensor functionalization platform providing antifouling properties, and many receptor integration options, uniquely including transmembrane proteins. Bilayer-coated sensors enable the kinetic characterization of membrane/analyte interactions. Addressed theoretically and experimentally is the self-assembly of model membranes on plasmonic sensors. Two categories of plasmonic sensors are studied in two parts. Part I aims to deposit raft-forming bilayers on planar nanoaperture arrays suitable for multiplexing and device integration. By vesicle fusion, planar bilayers are self-assembled on thiol-acid modified flame-annealed gold without the need for specific lipid head-group requirements. Identification of coexisting lipid phases is accomplished by AFM imaging and force spectroscopy mapping. These methods are successfully extended to metallic, plasmon-active nanohole arrays, nanoslit arrays and annular aperture arrays, with coexisting phases observed among the holes. Vis-NIR transmission spectra of the arrays are measured before and after deposition, indicating bilayer detection. Finally, the extraction of membrane proteins from cell cultures and incorporation into model supported bilayers is demonstrated. These natural membrane proteins potentially act as lipid-bound surface receptors. Part II aims to encapsulate in model lipid bilayers, metallic nanoparticles, which are used as probes in surface enhanced Raman spectroscopy. Three strategies of encapsulating particles, and incorporating Raman-active dyes are demonstrated, each using a different dye: malachite green, rhodamine-PE, and Tryptophan. Dye incorporation is verified by SERS and the bilayer is visualized and measured by TEM, with support from DLS and UV-Vis spectroscopy. In both parts, lipid-coated sensors are successfully fabricated and characterized. These results represent important and novel solutions to the functionalization of plasmonic surfaces with biologically relevant cell membrane mimics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".