Geometries, Stabilities, and Vibrational Properties of Bimetallic Mo<sub>2</sub>-Doped Ge<i><sub>n</sub></i> (<i>n</i> = 9−15) Clusters: A Density Functional Investigation
Bibliographic record
Abstract
Geometries of the bimetallic Mo2Gen (n = 9-15) clusters have been investigated systematically with the density functional approach. The relative stabilities and charge-transfer and vibrational properties of these clusters are presented and discussed. The dominant geometries of Mo2Gen (n = 9-12) clusters can be described as one Mo atom inside a Ge cage and another Mo atom on the surface at smaller sizes with n = 9-12. Interestingly, the stable geometry of Mo2Ge9 cluster has the framework which is analogous to a recent experimental observation (Goicoechea, J. M.; Sevov, S. C. J. Am Chem. Soc. 2006, 128, 4155). The calculated fragmentation energies and the obtained relative stabilities demonstrate that the remarkable Mo2-doped Ge12 is the most stable species of all different sized clusters. The critical size of Mo2-encapsulated cagelike germanium clusters appears at n = 15. The largest energy gap and strongest stability of Mo2Ge12 enable this species to be a unit of multiple metal Mo-doped germanium nanotubes. Vibrational mode analyses of Mo2Gen clusters demonstrate that the Mo-Mo stretching vibrations are sensitive to the geometries of the germanium frame, and that the point-group symmetry of germanium clusters can vary the Mo-Mo stretching vibration relative to the IR inactive vibration.
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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 | 0.001 | 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".