Crystallographic and structural characterizationof heterometallic platinum clusters Part VIII.Heteronona- and heterodecanuclear clusters
Bibliographic record
Abstract
Abstract This review classifies and analyses fifty heteronona- and heterodecanuclear Pt clusters of metal composition: Pt4Ru5, Pt3Ru6, Pt20sr PtRh8, PtAu8; Pt6M4, Pt5M5, Pt4M6, Pt3M2, Pt2M8, PtM9, Pt3Ru6M and PtAu8M. There are nine different heterometals: M = Ru, Au, Ag, Cu, Hg, Os, Rh, Ir and Fe, of which Ru and Au are the most frequent. The clusters crystallize mostly into two crystal classes, monoclinic (74%) and triclinic (18%), and their structures are complex. Three triangular layers of nine metal atoms arranged in the form of a face-shared bioctahedron are common in the series of heterononanuclear clusters. In the series of heterodecanuclear clusters distorted skeletal icosahedrons, where a central platinum atom is surrounded by nine metal atoms, and face (edge) shared (fused) bioctahedral cluster of the metal atoms are the most common. The most frequent ligands are CO and PPh3. The shortest metal-metal bond distances are: 2.540(4) Å (Pt-Fe), 2.580(2) Å (Ru-Ru), 2.584 Å (Pt-Pt) and 2.629(4) Å (Cu-Au). Several relationships between the structural parameters were found and are discussed. Some clusters contain two crystallographically independent molecules within the same crystal and are examples of distortion isomerism.
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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 | 0.001 | 0.001 |
| 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".