Bibliographic record
Abstract
The formation of nanocavities in InP(001) by room-temperature He implantation and subsequent thermal annealing was studied using a combination of high-resolution x-ray diffraction (HRXRD) and cross-sectional transmission electron microscopy (XTEM) analyses. The nanocavities size and depth distributions were measured as a function of He ion dose φHe (1×1016 to 9×1016 cm−2) and ion energy E (25 to 70 keV), as well as annealing temperature Ta (600 to 750 °C) and time ta (5 to 25 min). HRXRD scans from annealed samples indicate an expansion of the InP lattice, contrary to what is usually observed following heavy-ion implantation. The critical φHe and Ta values for the formation of nanocavities were found by XTEM analysis to be between 1 and 2×1016 cm−2 and between 600 and 620 °C, respectively. Cavities of diameter 4–50 nm with {110}, {101}, and {001} facets were obtained. Increasing Ta and ta resulted in larger cavities and increasing φHe produced a larger number of cavities. Furthermore we find that nanocavities are metastable as their size first increases with annealing temperature and time but then decreases until they disappear for ta>25 min at Ta=640 °C or ta>10 min at Ta=750 °C. Results are compared with similar work carried out on He-implanted silicon and differences between the two materials are explained in terms of defect diffusivity and surface energy, higher diffusivity enhancing cavity collapse and lower surface energy enhancing cavity growth.
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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".