Manganese phosphide nanoclusters embedded in epitaxial gallium phosphide grown from the vapor phase: Non-negligible role of Mn diffusion in growth kinetics
Bibliographic record
Abstract
Orthorhombic MnP nanoclusters are formed in GaP epitaxial films grown by metalorganic vapor phase epitaxy on GaP(001) substrates, which are labeled as GaP:MnP/GaP(001). Polycrystalline MnP films have also been grown from the vapor phase on GaP substrates and are labeled as (p-c)MnP/GaP(001). Both GaP:MnP/GaP(001) epilayers and (p-c)MnP/GaP (001) films show a very rich texture, which has been previously characterized by three dimensional x-ray diffraction reciprocal space maps combined with transmission electron microscopy measurements. Heterostructures (HSs) containing multiple layers of (p-c)MnP/GaP and of GaP:MnP/GaP have been designed and grown with the same process. These HSs add new elements to our understanding of the growth mechanisms involved in these complex systems. In particular, it is shown that Mn diffusion during growth is strongly enhanced leading to a picture of MnP cluster coalescence, which explains some of their properties, such as the variation of their spatial distribution within the GaP matrix with the epilayer thickness. We report an Mn atomic diffusion coefficient of (1.5 ± 0.2) × 10−15 cm2/s in these films at 650 °C. The data are compatible with the superdiffusion of Mn, where the square of the diffusion length as a function of time (t) obeys λD2∝t1+α with an estimated value of α≈0.52.
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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) | 0.000 | 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".