Band Gap Tuning in ZnO Through Ni Doping via Spray Pyrolysis
Bibliographic record
Abstract
The tuning of semiconductor band gaps can often provide significant performance increases and new applications for electronic, optoelectronic, and photocatalytic devices. Here, we study the band gaps of pure and nickel-doped zinc oxide thin films synthesized using the low-cost spray pyrolysis deposition method. Nickel concentration is varied from 0 to 15%, and the effects that this doping has on the electronic structure are analyzed. Using optical and synchrotron X-ray techniques, two regimes of band gap reduction via Ni doping are uncovered. For doping up to 4% Ni, there is a strong reduction in the gap, while continued doping up to 15% further reduces the gap, but to a lesser extent. The results are explained using X-ray spectroscopy and an Anderson impurity model. These tools show that the low doping case is driven by the interaction of the Ni 3d and O 2p states in both the valence and conduction bands. At high doping, the removal of Zn 3d states from the valence band and the change in Ni coordination from T d to O h both contribute to counteract the gap reduction. These results show how Ni can be used to tune the ZnO band gap over a large range useful for many applications.
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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) | 0.000 | 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".