Functionalization of germanene by metal atoms adsorption: A first-principles study
Bibliographic record
Abstract
First-principles calculations were performed to study the adsorption characteristics of 15 different metal atoms on germanene. For the alkali metal adatoms (Li, Na, and K) on germanene, the bonding is approximately ideal ionic and the semimetallic germanene finally becomes metallic with a small band gap opening at the Dirac point. The bonding of alkaline earth metal atoms (Be, Mg, and Ca) to germanene is a mixture of ionic and covalent. The Be and Mg adsorptions lead to semiconducting behavior in germanene, while similar to Li, Na, and K adsorptions, the Ca adsorbed germanene is metallic. For most transition metal adatoms, a strong covalent bonding behavior is found between the adatom and germanene layer, which causes much larger distortions in the germanene lattice. As a result of partially occupied d orbital, the transition metals show also diverse electronic structures when interacting with germanene, such as nonmagnetic metal, nonmagnetic semiconductor, ferromagnetic metal, ferromagnetic semiconductor, and more particularly, ferromagnetic half-metal. In addition, the analysis of the partial density of states indicates that the ferromagnetic property of the obtained transition metal – germanene systems mainly results from the spin-split of the adatom 3d states. The rich electronic and magnetic properties of metal–germanene systems may have potential applications for designing new nanoscale electronic and spintronic devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".