Magnetic hysteresis control in thin film Fe/Si multilayers by incorporation of B4C
Bibliographic record
Abstract
Magnetic hysteresis properties in Fe/Si multilayers have been studied as a function of the B 4 C content to control magnetization amplitude, coercivity, and hysteresis tilt, properties that are beneficial to tune for advancing applications in e.g. data storage, spintronics, and sensors. With an ion-assisted magnetron sputtering technique, 35 distinct thin film multilayer samples were prepared and their magnetic and structural properties were characterized by vibrating sample magnetometry, X-ray photoelectron spectroscopy, near edge X-ray absorption fine structure spectroscopy, and X-ray and neutron scattering methods. Key findings indicate that adding B 4 C lowers the coercivity and can decrease the saturation magnetization, demonstrating the tunability of magnetic responses based on composition. For samples with Λ=30Å periodicity, 10–15 % of B 4 C addition produces antiferromagnetically (AF) coupled multilayers, and such AF coupling strength increases with the B 4 C content. Our findings reveal that B atoms do not chemically bind within the Fe atoms but instead occupy interstitial positions, disrupting medium- to long-range crystallinity thereby inducing the amorphization. Thereon, the observed effects on magnetic properties are directly attributed to this amorphization process caused by the presence of B 4 C. The demonstrated ability to finely adjust magnetic properties by varying the B 4 C content offers a promising approach to overcome challenges in magnetic device performance and efficiency.
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) | 0.000 | 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".