Self-Patterned Mixed Phospholipid Monolayers for the Spatially Selective Deposition of Metals
Bibliographic record
Abstract
Metal-reactive organosulfur groups were patterned onto mica and silicon surfaces by dewetting instabilities during the Langmuir-Blodgett (LB) deposition of phase-separated mixed phospholipid monolayers. Monolayers were formed from binary mixtures of dipalmitoylphosphatidylcholine (DPPC), dilauroylphosphatidylcholine (DLPC), and their ω-methyldisulfide-dialkylphosphatidylcholine analogues, DSDPPC and DSDLPC. Patterns of highly parallel stripes of condensed DPPC or DSDPPC, protruding by 0.7-0.9 nm from a fluid matrix of DLPC or DSDLPC, were observed over areas extending at least 30 × 30 μm(2) in the LB films. The average stripe width varied from ca. 150 to 500 nm, depending on the lipid composition and deposition pressure. X-ray photoelectron spectroscopy confirmed that the phospholipid-monolayer-bound methydisulfides react with Au vapor to form a gold-thiolate species. The adsorption of thermally evaporated Au, Ag, and Cu onto DSDPPC/DLPC and DPPC/DSDLPC patterns was investigated by field emission gun scanning electron microscopy (FEGSEM) and atomic force microscopy (AFM). A change in phase contrast is observed in FEGSEM and AFM over the methyldisulfide-functionalized areas following metal deposition due to metal-thiolate bond formation. An increase in step height between the DSDPPC stripes and nonfunctionalized DLPC background following metal deposition, as well as the resistance of the metal-coated DSDPPC or DSDLPC regions to detergent extraction from the surface, attest to a selective metallization of the pattern. Our results indicate that the preferential adsorption of vapor-deposited metal onto the ω-methyldisulfide-terminated phase occurs at submonolayer coverages. The chemical reactivity exhibited by the organosulfur-modified phospholipid LB films make these templates potentially interesting for the fabrication of solid-supported patterns of metal nanostructures.
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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".