Engineering the defect distribution in ZnO nanorods through laser irradiation
Bibliographic record
Abstract
In recent years, defect engineering has shown great potential to improve the properties of metal oxide nanomaterials for various applications thus received extensive investigations. While traditional techniques mostly focus on controlling the defects during the synthesis of the material, laser irradiation has emerged as a promising post-deposition technique to further modulate the properties of defects yet there is still limited information. In this article, defects such as oxygen vacancies are tailored in ZnO nanorods through nanosecond (ns) laser irradiation. The relation between laser parameters and the temperature rise in the ZnO due to laser heating was established based on the observation in the SEM and the simulation. Raman spectra indicated that the concentration of the oxygen vacancies in the ZnO is temperature-dependent and can be controlled by changing the laser fluence and exposure time. This is also supported by the absorption spectra and the photoluminescence spectra of ZnO NRs irradiated under these conditions. On the other hand, the distribution of the oxygen vacancies was studied by XPS depth profiling, and it was confirmed that the surface-to-bulk ratio of the oxygen vacancies can be modulated by varying the laser fluence and exposure time. Based on these results, four distinctive regimes containing different ratios of surface-to-bulk oxygen vacancies have been identified. Laser-processed ZnO nanorods were also used as the catalyst for the photocatalytic degradation of rhodamine B (RhB) dye to demonstrate the efficacy of this laser engineering technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".