Orthogonal Functionalization of Nanoporous Substrates: Control of 3D Surface Functionality
Bibliographic record
Abstract
Anodic aluminum oxide (AAO) membranes with aligned, cylindrical, nonintersecting pores were selectively functionalized in order to create dual-functionality substrates with different pore-rim and pore-interior surface functionalities, using silane chemistry. We used a two-step process involving an evaporated thin gold film to protect the underlying surface functionality of the pore rims. Subsequent treatment with oxygen plasma of the modified AAO membrane removed the unprotected organic functional groups, i.e., the pore-interior surface. After gold removal, the substrate became optically transparent, and displayed two distinct surface functionalities, one at the pore-rim surface and another at the pore-interior surface. We achieved a selective hydrophobic functionalization with dodecyl-trichlorosilane of either the pore rims or the pore interiors. The deposition of planar lipid membranes on the functionalized areas by addition of small unilamellar vesicles occurred in a predetermined fashion. Small unilamellar vesicles only ruptured upon contact with the hydrophobic substrate regions forming solid supported hybrid bilayers. In addition, pore-rim functionalization with dodecyl-trichlorosilane allowed the formation of pore-spanning hybrid lipid membranes as a result of giant unilamellar vesicle rupture. Confocal laser scanning microscopy was employed to identify the selective spatial localization of the adsorbed fluorescently labeled lipids. The corresponding increase in the AAO refractive index due to lipid adsorption on the hydrophobic regions was monitored by optical waveguide spectroscopy. This simple orthogonal functionalization route is a promising method to control the three-dimensional surface functionality of nanoporous films.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".