Surface Area Characterization of Obliquely Deposited Metal Oxide Nanostructured Thin Films
Bibliographic record
Abstract
The glancing angle deposition (GLAD) technique is used to fabricate nanostructured thin films with high surface area. Quantifying this property is important for optimizing GLAD-based device performance. Our group has used high-sensitivity krypton gas adsorption and the complementary technique of cyclic voltammetry to measure surface area as a function of deposition angle, thickness, and morphological characteristics for several metal oxide thin films. In this work, we studied amorphous titanium dioxide (TiO(2)), amorphous silicon dioxide (SiO(2)), and polycrystalline indium tin oxide (ITO) nanostructures with vertical and helical post morphologies over a range of oblique deposition angles from 0 to 86 degrees. Krypton gas sorption isotherms, evaluated using the Brunauer-Emmettt-Teller (BET) method, revealed maximum surface area enhancements of 880 +/- 110, 980 +/- 125, and 210 +/- 30 times the footprint area (equivalently 300 +/- 40, 570 +/- 70, and 50 +/- 6 m(2) g(-1)) for vertical posts TiO(2), SiO(2), and ITO. We also applied the cyclic voltammetry technique to these ITO films and observed the same overall trends as seen with the BET method. In addition, we applied the BET method to the measurement of helical films and found that the surface area trend was shifted with respect to that of vertical post films. This revealed the important influence of the substrate rotation rate and film morphology on surface properties. Finally, we showed that the surface area scales linearly with film thickness, with slopes of 730 +/- 35 to 235 +/- 10 m(2) m(-2) microm(-1) found for titania vertical post films deposited at angles from 70 to 85 degrees. This characterization effort will allow for the optimization of solar, photonic, and sensing devices fabricated from thin metal oxide films using GLAD.
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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 | 0.000 |
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".