Robust Room Temperature Ferromagnetism In Cobalt Doped Graphene by Precision Control of Metal Ion Hybridization
Bibliographic record
Abstract
Abstract Graphene‐based magnetic materials exhibit novel properties and promising applications in the development of next‐generation spintronic devices. Modern synthesis techniques have paved the way to design precisely the local environments of metal atoms anchored onto a nitrogen‐doped graphene matrix. Herein, it is demonstrated that grafting cobalt (Co) into the graphene lattice induces robust and stable room‐temperature ferromagnetism. These comprehensive experiments and first‐principles calculations unambiguously identify that the mechanism for this unusual ferromagnetism is π‐d orbital hybridization between Co dxz and graphene pz orbitals. Here, it is found that the magnetic interactions of Co–carbon ions are mediated by the spin‐polarized graphene pz orbitals, and room temperature ferromagnetism can be stabilized by electron doping. It is also found that the electronic structure near the Fermi level, which sets the nature of spin polarization of graphene pz bands, strongly depends on the local environment of the Co moiety. This is the crucial, previously missing, ingredient that enables control of the magnetism. Overall, these observations unambiguously reveal that engineering the atomic structure of metal‐embedded graphene lattices through careful d to p orbital interactions opens a new window of opportunities for developing graphene‐based spintronics devices.
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".