Effect of High Zn Concentration on the Structural, Electrical, and Magnetic Properties of Zn-Doped Yttrium Iron Garnet Nanoparticles
Bibliographic record
Abstract
Abstract 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 Zn2+-doped YIG nanoparticles and nanocomposites (Y3Fe5–2x3+Fex4+ZnxO12) 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. Y3Fe4.7Zn0.3O12 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".