Microstructure control and property switching in stress-free van der Waals epitaxial VO2 films on mica
Bibliographic record
Abstract
Realizing stress-free inorganic epitaxial films on weakly bonding substrates is of importance for applications that require film transfer onto surfaces that do not seed epitaxy. Film-substrate bonding is usually weakened by harnessing natural van der Waals layers (e.g., graphene) on substrate surfaces, but this is difficult to achieve in non-layered materials. Here, we demonstrate van der Waals epitaxy of stress-free films of a non-layered material VO2 on mica. The films exhibit out-of-plane 010 texture with three in-plane orientations inherited from the crystallographic domains of the substrate. The lattice parameters are invariant with film thickness, indicating weak film-substrate bonding and complete interfacial stress relaxation. The out-of-plane domain size scales monotonically with film thickness, but the in-plane domain size exhibits a minimum, indicating that the nucleation of large in-plane domains supports subsequent island growth. Complementary ab initio investigations suggest that VO2 nucleation and van der Waals epitaxy involves subtle polarization effects around, and the active participation of, surface potassium atoms on the mica surface. The VO2 films show a narrow domain-size-sensitive electrical-conductivity-temperature hysteresis. These results offer promise for tuning the properties of stress-free van der Waals epitaxial films of non-layered materials such as VO2 through microstructure control.
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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".