Poster OPTICAL PROPERTIES OF NANOSTRUCTURED POROUS THIN FILMS FABRICATED USING GLANCING ANGLE DEPOSITION
Bibliographic record
Abstract
Glancing angle deposition (GLAD) is a thin film fabrication technique that has been developed at the University of Alberta. Films formed by physical vapor deposition (PVD) will exhibit a columnar nanostructure if the incident vapor flux arrives at an oblique angle with respect to the substrate surface. At a glancing angle of incidence (>70°), self-shadowing mechanisms become accentuated, resulting in a highly porous thin film composed of isolated columns that are inclined towards the incoming evaporation flux. The GLAD technique uses computer-controlled substrate motion to shape these isolated columns into advanced structures such as helices, zig-zags, and vertical posts [1-3]. The GLAD technique is very versatile. GLAD films can be formed from insulators, metals, and semiconductors using evaporation, pulsed laser deposition [4], or long throw, low pressure sputtering [5]. To illustrate the versatility of the process, a multilayer GLAD film, formed from SiO2 and TiO2, is shown in Fig. 1. GLAD films are also very robust to micromachining and post-processing. For example, filling the pores of a GLAD film with a curable polymer and etching out the original film structure leads to the formation of inverted GLAD coatings [6].
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) | 0.000 | 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".