Substrate Morphology Directs (001) Sb<sub>2</sub>Se<sub>3</sub> Thin Film Growth by Crystallographic Orientation Filtering
Bibliographic record
Abstract
Abstract Antimony chalcogenide, Sb2X3 (X = S, Se), applications greatly benefit from efficient charge transport along covalently bonded (001) oriented (Sb4X6)n ribbons, making thin film orientation control highly desirable – although particularly hard to achieve experimentally. Here, it is shown for the first time that substrate nanostructure plays a key role in driving the growth of (001) oriented antimony chalcogenide thin films. Vapor Transport Deposition of Sb2Se3 thin films is conducted on ZnO substrates whose morphology is tuned between highly nanostructured and flat. The extent of Sb2Se3 (001) orientation is directly correlated to the degree of substrate nanostructure. These data showcase that nanostructuring a substrate is an effective tool to control the orientation and morphology of Sb2Se3 films. The optimized samples demonstrate high (001) crystallographic orientation. A growth mechanism for these films is proposed, wherein the substrate physically restricts the development of undesirable crystallographic orientations. It is shown that the surface chemistry of the nanostructured substrates can be altered and still drive the growth of (001) Sb2Se3 thin films – not limiting this phenomenon to a particular substrate type. Insights from this work are expected to guide the rational design of Sb2X3 thin film devices and other low‐dimensional crystal‐structured materials wherein performance is intrinsically linked to morphology and orientation.
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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".