Bibliographic record
Abstract
Magnetic nanoscale devices have shown great promise in both research and industry. Magnetic nanostructures have potential for non-volatile data storage applications, reconfigurable logic devices, biomedical devices and many more. \n \nThe S-state magnetic element is one of the promising structures for non-volatile data storage applications and reconfigurable logic devices. It is a single-layer logic element that can be integrated in magnetoresistive structures. We present a detailed micromagnetic analysis of the geometrical parameter space in which the logic operation is carried out. The influence of imperfections, such as sidewall roughness and roundness of the edge is investigated. \n \nMagnetic nanowires are highly attractive materials that has potential for applications in ultrahigh magnetic recording, logic operation devices, and micromagnetic and spintronic sensors. To utilize applications, manipulation and assembly of nanowires into ordered structures is needed. Magnetic self-alignment is a facile technique for assembling nanowires into hierarchical structures. In my thesis, I focus on synthesizing and assembling nickel nanowires. The magnetic behaviour of a single nickel nanowire with 200~nm diameter is investigated in micromagnetic simulations. Nickel nanowires with Au caps at the ends were synthesized by electrochemical deposition into nanopores in alumina templates. One-dimensional alignment, which forms chains and two-dimensional alignment, which forms T-junctions as well as cross-junctions are demonstrated. Attempts to achieve three-dimensional alignment were not successful yet. I will discuss strategies to improve the alignment process.
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 | 0.001 | 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) | 0.006 | 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".