Ferromagnetic Multilayers: Magnetoresistance, Magnetic Anisotropy, and Beyond
Bibliographic record
Abstract
Obtaining highly sensitive ferromagnetic, FM, and nonmagnetic, NM, multilayers with a large room-temperature magnetoresistance, MR, and strong magnetic anisotropy, MA, under a small externally applied magnetic field, H, remains a subject of scientific and technical interest. Recent advances in nanofabrication and characterization techniques have further opened up several new ways through which MR, sensitivity to H, and MA of the FM/NM multilayers could be dramatically improved in miniature devices such as smart spin-valves based biosensors, non-volatile magnetic random access memory, and spin transfer torque nano-oscillators. This review presents in detail the fabrication and characterization of a few representative FM/NM multilayered films—including the nature and origin of MR, mechanism associated with spin-dependent conductivity and artificial generation of MA. In particular, a special attention is given to the Pulsed-current deposition technique and on the potential industrial applications and future prospects. FM multilayers presented in this review are already used in real-life applications such as magnetic sensors in automobile and computer industries. These material are extremely important as they have the capability to efficiently replace presently used magnetic sensors in automobile, electronics, biophysics, and medicine, among many others.
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 | 0.001 | 0.001 |
| 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".