How many bits may fit in a single magnetic dot? XMCD-PEEM evidences the switching of Néel caps inside Bloch domain walls
Bibliographic record
Abstract
Data storage relies on the handling of two states, called bits. The market of mass storage is currently still dominated by magnetic technology, hard disk drives for the broad public and tapes for massive archiving. In these devices each bit is stored in the form of the direction of magnetization of nanosized magnetic domains, i.e. areas of ferromagnetic materials displaying a uniform magnetization. While miniaturization is the conventional way to fuel the continuous increase of device density, disruptive solutions are also sought. To these pertain in recent years many fundamental studies no longer considering the magnetic domains themselves, but the manipulation of the domain walls (DWs) that separate such domains. Concepts of storage and logic based on the propagation of DWs along lithographically-patterned stripes have been patented, while many fundamental aspects of DW propagation deeply related to condensed matter physics are still hotly debated. If one now considers magnetic dots of submicrometer dimensions, the magnetization has a tendency to curl along the outer edges of the nanostructure to close its magnetic flux and thereby reduce its magnetostatic energy. Then both domains and DWs of well-defined geometries arise, whose combined manipulation has been proposed as a multilevel magnetic storage scheme...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".