Bibliographic record
Abstract
The properties of several two-dimensional (2d) materials are studied. The main content is the study of magnetic impurities in graphene through phase coherent transport phenomena, including weak localization (WL) and universal conductance fluctuations (UCF). Magnetic impurities manifest themselves in the in-plane magnetic field and temperature dependence of the dephasing (phase breaking) rate. Our experiments unambiguously reveal the existence of magnetic impurities through the in-plane magnetic field dependence of WL and UCF. The properties of the magnetic impurities are further studied through the dephasing rate as a function of magnetic field and temperature. The WL dephasing rate as a function of in-plane magnetic field shows a non-monotonic behaviour, which is rooted in the existence of magnetic impurities with a Landé g factor different from that of the free electron. The collapse of the dephasing rate as a function of temperature is a sign of the quenching of magnetic impurities, which could come from Kondo coupling between magnetic impurities and conduction electrons or Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling between magnetic impurities. The implications of our graphene experiments are two-fold: on the one hand, they provide new knowledge about the interplay among magnetic moments and electrons in graphene; on the other hand, they also established an effective tool to reveal the existence of magnetic impurities in 2d systems. In addition to graphene, we also studied the exfoliation and stability of thin sheets of FeSe -- a member of the Fe-based superconductors family. Our Raman Spectroscopy, Atomic Force Microscopy, Optical Microscopy and Time-of-Flight-Secondary-Ion-Mass-Spectroscopy experiments show that FeSe nanosheets decay in air, precipitation of Se and oxidation likely occurring during the decay process. Our transport measurements show that FeSe nanosheets exposed briefly to air can still retain superconductivity.
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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 | 0.000 |
| 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".