Glancing angle deposition: Fabrication, properties, and applications of micro- and nanostructured thin films
Bibliographic record
Abstract
Physical vapor deposition under conditions of obliquely incident flux and limited adatom diffusion results in a film with a columnar microstructure. These columns will be oriented toward the vapor source and substrate rotation can be used to sculpt the columns into various morphologies. This is the basis for glancing angle deposition (GLAD), a technique for fabricating porous thin films with engineered structures. The origin of the columnar structure characteristic of GLAD films is discussed in terms of nucleation processes and structure zone models. As deposition continues, the columnar structures are influenced by atomic-scale ballistic shadowing and surface diffusion. Competitive growth is observed where the tallest columns grow at the expense of smaller features. The column shape evolves during growth, and power-law scaling behavior is observed as shown in both experimental results and theoretical simulations. Due to the porous nature of the films and the increased surface area, a variety of chemical applications and sensor device architectures are possible. Because the GLAD process provides precise nanoscale control over the film structure, characteristics such as the mechanical, magnetic, and optical properties of the deposited film may be engineered for various applications. Depositing onto prepatterned substrates forces the columns to adopt a planar ordering, an important requirement for photonic crystal applications.
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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".