Flux Engineering To Control In-Plane Crystal and Morphological Orientation
Bibliographic record
Abstract
We tailored nanostructured morphology and crystal texture of iron nanocolumns by engineering the inclination and azimuthal directions of the collimated flux characteristic of glancing angle deposition (GLAD). Under continuous substrate rotation, the flux is azimuthally isotropic within one rotation. With large substrate rotation speeds, we can deposit vertical nanocolumns with a faceted, tetrahedral apex, BCC crystal structure and ⟨111⟩ fiber texture. Designing the flux to have an azimuthal 3-fold symmetry, which reflects the symmetry of the tetrahedral apex, allows us to induce both an in-plane and out-of-plane texture (biaxial texture) by evolutionary selection. In-plane crystal orientation is accompanied by a preferential azimuthal nanocolumn orientation, where the sides of tetrahedral apex are directed toward the flux direction. This work demonstrates the flux engineering technique, which can orient in-plane crystal texture and morphology of crystalline nanocolumns on amorphous substrates. This control is a useful addition to vapor–solid, physical self-assembly with the potential to improve the performance of porous thin film architectures as biaxial buffer layers, and in a variety of device applications such as photovoltaics and energy storage.
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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".