ZnO nanowire arrays synthesized on ZnO and GaN films for photovoltaic and light-emitting devices
Bibliographic record
Abstract
The wide bandgap, one-dimensional zinc oxide (ZnO) nanowires (NWs) and their heterostructures with other materials provide excellent pathways for efficient photovoltaic (PV) and light-emitting devices. ZnO NWs sensitized with quantum dots (QDs) provide high-surface area and tunable bandgap absorbers with a directional path for carriers in advanced PV devices, while ZnO heterojunctions with other p-type wide bandgap materials lead to light-emitting diodes (LEDs) with better emission and waveguiding properties compared with the homojunction counterparts. Synthesis of the structures with the desired morphology is a key to device applications. In this work, ZnO NW arrays were synthesized using hydrothermal method on ZnO and GaN thin films. Highly crystalline, upright, and ordered arrays of ZnO NWs in the 50 to 250-nm diameter range and 1 μm in length were obtained. The morphology and optical properties of the NWs were studied. Energy dispersive x-ray spectroscopy (EDX) analysis revealed nonstoichiometric oxygen content in the grown ZnO NWs. Photoluminescence (PL) studies depicted the presence of oxygen vacancy and interstitial zinc defects in the grown ZnO NWs, underlining the potential for LEDs. Further, hydrophobically ligated CdSe/ZnS QDs were successfully incorporated to the NW arrays. PL analysis indicated the injection of electrons from photoexcited QDs to the NWs, showing the potential for quantum dot-sensitized solar cells.
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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".