Tuning the Structural, Electronic, and Magnetic Properties of Germanene by the Adsorption of 3d Transition Metal Atoms
Bibliographic record
Abstract
The structural, electronic, and magnetic properties of 3$d$ transition metal (TM) atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) adsorbed germanene are addressed using density functional theory. Based on the adsorption energy, TM atoms prefer to occupy at the hollow site for all the cases. The obtained values of the total magnetic moment vary from 0.97 $\mu_B$ to 4.95 $\mu_B$ in case of Sc to Mn-adsorption, respectively. A gap of 74 meV with a strongly enhanced splitting of 67 meV is obtained in case of Sc-adsorption, whereas metallic states are obtained in case of Ti, Cr, Mn, Fe, and Co. Non-magnetic states are realized for Ni, Cu, and Zn-adsorption. Moreover, semiconducting nature is obtained for non-magnetic cases with a gap of 26 to 28 meV. Importantly, it is found that V-adsorbed germanene can host the quantum anomalous Hall effect. The obtained results demonstrate that TM atoms and nearest neighbour Ge atoms are ferro-magnetically ordered in the cases of V, Mn, Fe, Co, Ni, Cu, and Zn, while anti-ferromagnetic ordering is obtained for Sc, Ti, and Cr. In addition, the effects of the coverage of all TM atoms on the electronic structure and the ferro-magnetic and anti-ferro-magnetic coupling in case of Mn are examined. The results could help to understand the effect of TM atoms in a new class of two-dimensional materials beyond graphene and silicene.
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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".