Magnetic Signature in Graphene Using Adsorbed Metal–Organic Networks
Bibliographic record
Abstract
The interaction of a 2D metal–organic network (MON) stacked on graphene has been studied with the help of first-principles density functional theory (DFT) and DFT + U calculations. By varying the length of a polyphenyl-dicarbonitrile linker, we have evaluated the influence of the metal–metal distance on the electronic and magnetic properties of the MON complexes. Although MON composed of small molecules shows a moderately stable ferromagnetic phase, this magnetic order drops with the size of the complex. After the adsorption of MON on graphene, this last becomes n-doped due to an important charge transfer that improves with the molecular unit size. The MON–graphene interaction contributes to drastically reduce the overall stability of any magnetic order, but the local charge transfer remains strongly spin-polarized-dependent. Hence, the adsorption of magnetic MON on graphene leads to the modification of the electronic and magnetic properties of graphene, mostly in a closed proximity region to the active metal atoms of the MON. Spin-polarized scanning tunneling microscopy simulations reveal a magnetic signature in graphene that originates from its interaction with the MONs and that could be experimentally observed.
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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".