Tuning Photoluminescence of Ge/GeO<sub>2</sub> Core/Shell Nanoparticles by Strain
Bibliographic record
Abstract
The distribution of strain field plays an important role in deciding the physical properties of nanocrystals. The growth strain of Ge/GeO 2 core/shell nanoparticles embedded in a regular array of Al 2 O 3 nanoparticles and its resulting effect on the optical properties are investigated. Two-dimensional finite element calculations clearly demonstrate that Ge/GeO 2 nanoparticles certainly experienced greater compressive strain in Al 2 O 3 nanoparticles than in Al 2 O 3 thin film, especially at the GeO 2 shell area. This may lead to much more strain-relaxing defects produced at the GeO 2 shell in Al 2 O 3 nanoparticles. Meanwhile, the photogenerated excitons/electron−hole pairs are localized by defects located at the GeO 2 shell and are forced to recombine while being spatially confined in the Al 2 O 3 nanoparticles. These effects might contribute to the observed intensity enhancement and blue shift of the photoluminescence peaks for the sample with Ge/GeO 2 core/shell nanoparticles embedded in Al 2 O 3 nanoparticles. The findings presented here provide physical insight and offer useful guidelines to controllably modify the optical properties of semiconductor nanoparticles through strain engineering.
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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".