Magnetoresistance effects in multilayer graphene as grown on ferromagnetic substrates and implications for spin filtering
Bibliographic record
Abstract
Multilayer graphene (MLG) or thin graphitic films as grown on nickel (Ni) or cobalt (Co) has been recently proposed as a promising platform for realizing highly efficient spin filters. However, graphene forms chemisorption interface with Ni and Co, which significantly affects the electronic properties of the interfacial layers as well as the growth of the subsequent graphene layers. Such systems can give rise to various types of magnetoresistance (MR) effects that are completely unrelated to spin filtering. It is, therefore, important to understand these MR effects in order to identify the spin filtering related signal. In this work we highlight on the various MR effects that are observed in Ni/MLG systems and that are also unrelated to spin filtering. In particular, an “interlayer magnetoresistance” (ILMR) effect manifests in these systems, which can result in large MR values that are comparable to state-of-the-art magnetic tunnel junctions at similar operating conditions. Preliminary measurements on Co/MLG samples also indicate presence of ILMR effect.
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) | 0.001 | 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".