The role of reduced graphene oxide capping on defect induced ferromagnetism of ZnO nanorods
Bibliographic record
Abstract
In this study, the effect of different numbers of layers of reduced graphene oxide (RGO) on the ferromagnetic behavior of zinc oxide-reduced graphene oxide (ZnO-RGO) hybrid architectures has been investigated. Scanning and transmission electron microscopy along with x-ray diffraction of these hybrids confirm that ZnO nanorods are wrapped with different numbers of layers of RGO in a controlled way and their hexagonal phase is unaffected by these layers. Raman and photoelectron spectroscopy of these hybrids reveals that RGO does not alter the nonpolar optical phonon E(2) (high) mode and chemical state of Zn(2+) in ZnO. Electron paramagnetic resonance (EPR) spectra show that RGO passivates singly charged oxygen vacancies (VOS⁺) in ZnO. It correlates the passivation efficiency of VOS⁺ to the number of RGO layers and this has been achieved up to 90% by ∼31 layers of RGO. Due to passivation of VOS⁺ in ZnO by RGO, the ferromagnetic behavior (saturation magnetization and divergence between zero field cooled and field cooled) in ZnO-RGO hybrids is suppressed as compared to ZnO. Combining the EPR and magnetic behavior, a direct link between the passivation of the singly charged oxygen vacancies present on the surface of ZnO nanorods and the number of RGO layers is established.
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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".