Deposition of ZnO Nanocrystals on Fe<sub>3</sub>O<sub>4</sub> Nanocubes and Their Special Luminescent and Magnetic Properties
Bibliographic record
Abstract
With increasing miniaturization, it is extremely important to maintain the magnetization stability at small scale. Herein, more efforts and interests focus on the interface of magnetic core and semiconductor shell to obtain desired magnetic and/or luminescent properties. Here, Fe3O4 nanocubes are synthesized via a thermal decomposition followed by coating ZnO nanocrystals. To create a large interface, large Fe3O4 nanocubes with 78 ± 3 nm average side‐length are synthesized through adjusting the ratio of iron precursor to stabilizer. The average diameter of the particular ZnO nanostructures coated on the nanocubic Fe3O4 is around 10 ± 2 nm. In addition to the photoluminescent properties of the ZnO‐coated nanostructures, core‐shell Fe3O4@ZnO nanostructures demonstrate enhanced UV absorption at 360 nm, which has a 20 nm blueshift compared to bulk ZnO. The superparamagnetic properties of Fe3O4@ZnO core–shell hybrid nanocrystals at room temperature are dominated by the ferromagnetic properties when the temperature is lower than the Blocking temperature, 235.7 K. The observed exchange bias and temperature‐dependent magnetization can result from the interfacial interphase between ZnO and Fe3O4. The anisotropy contributed by the interfacial interphase allows the nanostructures to maintain stable magnetization in miniaturized devices.
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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".