Bibliographic record
Abstract
Graphene was expected to prove useful in the field of spintronics because a long spin relaxation time (few micro second) was theoretically expected. However, experimental results using exfoliated graphene have shown that the spin relaxation time is a few orders of magnitude less than the theoretical prediction. It was discovered that the reason for this unexpected shorter spin relaxation time is the presence of magnetic moments on graphene and magnetic moments exist on most forms of graphene. Many theoretical articles expected these magnetic moments to arise due to graphene defects. However, it is not experimentally clear where and how they arise. To answer where and how, we investigates it with dephasing rate (phase relaxation rate) monitored via weak localization on graphene, grown by chemical vapour deposition (CVD graphene). The experiments are performed on field-effect devices made from CVD graphene on various substrates under perpendicularly applied magnetic fields at 4.2 K. The samples are thermally annealed under various conditions, which is a commonplace technique used to clean the surface of graphene. Only the gas annealing induces the additional source of dephasing rate on CVD graphene. However, this could not be seen in before-annealed samples and vacuum annealed samples. Additional experiment confirms that this additional source on gas annealed sample has the magnetic property. The result on this thesis can help answer the origin of magnetic moments on graphene.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".