Significant Spin‐Capacitive Modulation of Magnetism Through Na <sup>+</sup> Motion in Layered FeSe
Bibliographic record
Abstract
Abstract Recently, a novel spin‐capacitive method has emerged and distinguishes itself in voltage control of magnetism (VCM) through dual‐phase ion‐electron conduction, exhibiting significant, rapid, and reversible magnetic modulation in several lithium‐ion‐based devices, with promising potential for low‐power applications. Considering the inherent link to neuronal processing, enhanced safety, and superior compatibility with semiconductor integration of sodium‐based devices, the first report on magnetization modulation via Na‐assisted spin capacitance mechanism is presented, showcasing unique advantages over lithium‐based devices. Employing layered FeSe as the regulated material, operando magnetometry demonstrates giant and reversible magnetic modulation, involving On‐Off ferromagnetic switching at high voltages and quasi‐linear magnetization change at low voltages. Comprehensive analyses confirm the spin capacitance characteristics of VCM in low‐voltage regions, achieving a significant modulation amplitude of 12.70 emu g −1 within 1 V. This is facilitated by nearly 100% formation of Fe nanoparticles via the two‐step reaction during Na introduction to layered FeSe. Furthermore, spin‐capacitive magnetic regulation in the devices exhibits superior manipulative characteristics within the low voltage range of 0–0.75 V, notably robust endurance, rapid response, and non‐volatility. This work inspires new avenues for developing low‐power devices featuring high speed, reversibility, non‐volatility, and cost‐effectiveness, especially exhibiting notable advantages in brain‐like simulation applications.
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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".