Ionic Modulation of Interfacial Magnetism in Light Metal/Ferromagnetic Insulator Layered Nanostructures
Bibliographic record
Abstract
Abstract Ferromagnetic insulator thin film nanostructures are becoming the key component of the state‐of‐the‐art spintronic devices, for instance, yttrium iron garnet (YIG) with low damping, high Curie temperature, and high resistivity is explored into many spin–orbit interactions related spintronic devices. Voltage modulation of YIG, with great practical/theoretical significance, thus can be widely applied in various YIG‐based spintronics effects. Nevertheless, to manipulate ferromagnetism of YIG through electric field (E‐field), instead of current, in an energy efficient manner is essentially challenging. Here, a YIG/Cu/Pt layered nanostructure with a weak spin–orbit coupling interaction is fabricated, and then the interfacial magnetism of the Cu and YIG is modified via ionic liquid gating method significantly. A record‐high E‐field‐induced ferromagnetic resonance field shift of 1400 Oe is achieved in YIG (17 nm)/Cu (5 nm)/Pt (3 nm)/ionic liquid/Au capacitor layered nanostructures with a small voltage bias of 4.5 V. The giant magnetoelectric tunability comes from voltage‐induced extra ferromagnetic ordering in Cu layer, confirmed by the first‐principle calculation. This E‐field modulation of interfacial magnetism between light metal and magnetic isolator may open a door toward compact, high‐performance, and energy‐efficient spintronic 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) | 0.000 | 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 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".