Surface Functionalization of Porous Nanostructured Metal Oxide Thin Films Fabricated by Glancing Angle Deposition
Bibliographic record
Abstract
We report the application of solution- and vapor-phase siloxane-based methods for tailoring the surface chemistry/properties of highly porous, nanostructured thin films fabricated using glancing angle deposition (GLAD). The GLAD technique produces high surface area films consisting of isolated columns and provides complete control over the film/column morphology. In the present study, the chemical tunability of a variety of metal oxide GLAD films was investigated using solution-based and vapor-phase surface functionalization methodologies. The surface properties and structures of the treated and untreated films were investigated using scanning electron microscopy (SEM), advancing aqueous contact angle measurements, cyclic voltammetry, and X-ray photoelectron spectroscopy (XPS). Results indicate that the surface chemistry of metal oxide GLAD films could be tailored by either method; however, chemical reactivity depends strongly on the metal oxide film material. Chemical tunability is demonstrated through the covalent tethering of numerous chemical moieties onto the exposed and interior surfaces of metal oxide GLAD films of varied structural motifs. Through careful choice of surface modifier, the present derivatization methods afford a full range of aqueous wettability from hydrophilic to superhydrophobic without compromising film structure. These functionalized, nanoconstructed films demonstrate a high degree of tunability over both structural and surface properties, making them well suited for diverse applications such as optical filters or sensors.
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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) | 0.001 | 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 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".