Effect of High Zn Concentration on the Structural, Electrical, and Magnetic Properties of Zn-Doped Yttrium Iron Garnet Nanoparticles
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Current-induced magnetoelectric (ME) effect offers the potential for broadband, low-power tunable microwave devices. While yttrium iron garnet (YIG) is the most used ferrite due to its superior magnetic properties, its high-quality dielectric properties hinder its potential tuning with an electric current. To increase YIG’s conductivity, we investigated Zn 2+ -doped YIG nanoparticles and nanocomposites (Y 3 Fe 5–2 x 3+ Fe x 4+ Zn x O 12 ) synthesized using the sol–gel method within a broad concentration range of dopants (0 < x < 1.0). Herein, for the first time, we report the effect of high-level Zn doping on the electrical conductivity and ferromagnetic resonance (FMR) of a YIG nanocomposite material. An increase in Zn concentration resulted in the formation of the yttrium iron perovskite (YIP) phase, and for concentrations above 0.6, the sol–gel synthesis yielded the predominant formation of YIP. Y 3 Fe 4.7 Zn 0.3 O 12 had the highest Zn content when the garnet phase was predominantly formed during the synthesis. The increase in the Zn content in the lattice enhanced the conductivity of yttrium iron garnet doped with Zn (YIG:Zn) by up to 3 orders of magnitude compared to that of pure YIG. In addition, the increase in the Zn content yielded an increase in the domain and ferromagnetic resonance frequencies of the YIG:Zn material. Overall, highly doped YIG:Zn nanocomposites have the potential to enable current-induced ME due to their superior conductivity.
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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 | 0.000 |
| 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 it