Nitridation Temperature Effects on Electronic and Chemical Properties of (Ga<sub>1–<i>x</i></sub>Zn<sub><i>x</i></sub>)(N<sub>1–<i>x</i></sub>O<sub><i>x</i></sub>) Solid Solution Nanocrystals
Bibliographic record
Abstract
Solid solution nanocrystals of gallium nitride–zinc oxide have been realized as potential photocatalysts for visible light driven overall water splitting. The band gap of these materials has been found to narrow further into the visible region as a function of increasing zinc oxide concentration, and thus, it is desirable to synthesize zinc oxide-rich gallium nitride–zinc oxide solid solutions. In this paper, we discuss the effects of using nitridation temperature to control zinc oxide content on the electronic and chemical properties of gallium nitride–zinc oxide solid solution nanostructures. The effect of nitridation temperature was studied using X-ray absorption fine structure (XAFS), including both X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS), and X-ray excited optical luminescence (XEOL). It was determined that using nitridation temperature as a method of controlling zinc oxide concentration results in solid solutions with poor crystallinity, phase separation, and mixed surface oxide formation. These findings suggest that many complications arise from using nitridation temperature to control zinc oxide concentration in gallium nitride–zinc oxide solid solutions, and thus, it is possible that the resultant materials would exhibit poor photocatalytic activity.
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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".