Stability of a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Sn</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>tetrahedral cluster in an alkali atom environment
Bibliographic record
Abstract
The stability of a ${\mathrm{Sn}}_{4}$ tetrahedral cluster in an alkali atom environment is studied by calculations on a series of ${A}_{n}{\mathrm{Sn}}_{4}$ $(A=\mathrm{Li},$ Na, and K) clusters, in which, starting at $n=4,$ alkali atoms are added one by one until $n=10.$ The results show that the ${\mathrm{Sn}}_{4}$ tetrahedron is unstable in ${\mathrm{Li}}_{n}{\mathrm{Sn}}_{4}$ clusters, opening into a ${\mathrm{Sn}}_{4}$ butterfly at $n=5,$ and completely broken at $n=10.$ For ${\mathrm{K}}_{n}{\mathrm{Sn}}_{4}$ clusters, however, the ${\mathrm{Sn}}_{4}$ tetrahedron remains robust for all values of n, while ${\mathrm{Na}}_{n}{\mathrm{Sn}}_{4}$ clusters are an intermediate case in which the ${\mathrm{Sn}}_{4}$ tetrahedron opens into a butterfly at $n=8.$ The size of the alkali atoms and the strength of the bonding between them determine whether the ${\mathrm{Sn}}_{4}$ tetrahedron breaks up or not, and the ${\mathrm{Li}}_{2}$ unit is found to be responsible for cutting the Sn-Sn bonds of the ${\mathrm{Sn}}_{4}$ tetrahedron in ${\mathrm{Li}}_{n}{\mathrm{Sn}}_{4}$ clusters. The results are discussed in relation to the corresponding alkali-tin bulk alloys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.012 | 0.001 |
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".