Space‐Charge Control of Magnetism in Ferromagnetic Metals: Coupling Giant Magnitude and Robust Endurance
Bibliographic record
Abstract
Abstract Ferromagnetic metals show great prospects in ultralow‐power‐consumption spintronic devices, due to their high Curie temperature and robust magnetization. However, there is still a lack of reliable solutions for giant and reversible voltage control of magnetism in ferromagnetic metal films. Here, a novel space‐charge approach is proposed which allows for achieving a modulation of 30.3 emu/g under 1.3 V in Co/TiO 2 multilayer granular films. The robust endurance with more than 5000 cycles is demonstrated. Similar phenomena exist in Ni/TiO 2 and Fe/TiO 2 multilayer granular films, which shows its universality. The magnetic change of 107% in Ni/TiO 2 underlines its potential in a voltage‐driven ON–OFF magnetism. Such giant and reversible voltage control of magnetism can be ascribed to space‐charge effect at the ferromagnetic metals/TiO 2 interfaces, in which spin‐polarized electrons are injected into the ferromagnetic metal layer with the adsorption of lithium‐ions on the TiO 2 surface. These results open the door for a promising method to modulate the magnetization in ferromagnetic metals, paving the way toward the development of ionic‐magnetic‐electric coupled applications.
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".