(Invited) Gap Nanowires with Twinning Superlattices: Structure, Optical Properties and Applications
Bibliographic record
Abstract
Semiconductor nanowires (NWs) are a good candidate for future optoelectronic devices. However, the control of the essential parameters that determine the electronic and optical quality of NWs, such as crystal structure and incorporation of impurity dopants, are still challenging problems. Most III-V NWs exhibit crystal defects, which are typically randomly distributed zincblende twinning segments and stacking faults that can affect the optical and electrical properties of NW devices. The incorporation of intentional impurity dopants in NWs is important for the fabrication of p-n junctions and control of the electrical conductivity of NWs. The effect of Te and Be impurity dopant concentration on the crystal structure, surface roughness and optical properties of GaAs NWs will be presented. Four identical GaAs NW arrays were grown: an undoped sample (as a reference) and 6 samples with different Te and Be doping concentration. High resolution transmission electron microscopy (HRTEM) revealed an unusual superlattice twinning, with periodicity that became wider and more regular as the doping level increased. Twin boundaries in GaP are shown to act as an atomically narrow plane of wurtzite phase with a type-I homostructure band alignment. Twin boundaries and stacking faults (wider regions of the wurtzite phase) lead to the introduction of shallow trap states observed in photoluminescence studies. Controlling the surface roughness, the periodicity, and the width of twinning planes with doping concentration might open new possibilities for high efficiency NW-based thermoelectric devices, but also has wide implications for all NW 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".