Growth evolution of laser-ablated Sr2FeMoO6 nanostructured films: Effects of substrate-induced strain on the surface morphology and film quality
Bibliographic record
Abstract
Pulsed laser deposition was used to grow Sr(2)FeMoO(6) films of different thicknesses on MgO(100), SrTiO(3)(100), and LaAlO(3)(100) with respective lattice mismatches of +6.2%, -1.2%, and -4.3%. Surface roughness and morphology, and film crystal quality and epitaxy were determined by atomic force microscopy and x-ray diffraction, respectively. Two-dimensional layer-by-layer growth was evident for the Sr(2)FeMoO(6) grown on MgO and SrTiO(3) with the film becoming smoother with increasing thickness. The Sr(2)FeMoO(6) films had more nucleation sites on MgO than SrTiO(3). On LaAlO(3), however, three-dimensional progressive growth of flakelike Sr(2)FeMoO(6) nanostructures was observed for all film thicknesses. High-resolution x-ray diffraction measurements indicated that the Sr(2)FeMoO(6) films are near-epitaxial and c-axis oriented on all the substrates. Reciprocal space maps further revealed that Sr(2)FeMoO(6) grows on MgO with relatively constant lattice parameters with increasing film thickness. For films thicker than 120 nm, the formation of a second phase was observed on SrTiO(3) and LaAlO(3) but not on MgO, suggesting that the formation of a second phase provides an effective strain relief in the former. These results suggested a different growth mechanism for the Sr(2)FeMoO(6) films on MgO compared to the SrTiO(3) and LaAlO(3) substrates.
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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".