Enhancement of band edge luminescence in ZnSe nanowires
Bibliographic record
Abstract
In order to realize the full potential of nanowires for optical applications, it is essential to synthesize nanowires that can emit predominantly via band to band or band edge (BE) transitions. However, many compound semiconductor nanowires, irrespective of the method of their growth, contain a high density of native defects; these result in competing deep defect (DD) related emission, limiting their utility for optoelectronic device applications. The concentration of these native defect states depends on the gas phase stoichiometry. In this work, we report on the influence of gas phase stoichiometry on the structural and optical properties of single crystal zinc selenide (ZnSe) nanowires. We find that nanowires grown under stoichiometric conditions contain such defect states with associated weak BE emission and strong DD emission. However, nanowires grown under Zn-rich conditions were characterized by photoluminescence spectra dominated by strong BE emission while those grown under Se-rich conditions showed strong DD related emission. Hence, it is necessary to develop a strategy for enhancing the BE emission while simultaneously quenching the DD emission. We demonstrate a technique of postgrowth treatment that can effectively perform this function, and using this strategy the ratio of the BE/DD emission can be increased by a factor of several thousands, at least an order of magnitude higher than previously reported values. This reveals BE dominated photoluminescence in these nanowires and makes these nanowires suitable for developing future optoelectronic devices.
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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".