Crystallographic and structural characterization of heterometallic platinum compoundsPart VII. Heterohepta- and heterooctanuclear clusters
Bibliographic record
Abstract
Abstract This review classifies and analyzes over fifty heterohepta- and heterooctanuclear platinum clusters. There are eight types of metal combinations in heteroheptanuclear: Pt6M, Pt5M2, Pt4M3, Pt3M4, Pt2M5, PtM6, Pt3Hg2Ru2 and Pt2Os3Fe2. The seven metal atoms are in a wide variety of arrangements, with the most common being one in which the central M atom (mostly M(I)) is sandwiched by two M3 triangles. Another arrangement often found is an octahedron of M6 atoms asymmetrically capped by an M atom. The shortest Pt-M bond distances (non-transition and transition) are 2.326(1) Å (M = Ga) and 2.537(6) Å (M = Fe). The shortest Pt-Pt bond distance is 2.576(2) Å. In heterooctanuclear platinum clusters there are eight types of metal combinations: Pt6M2, Pt4M4, Pt3Ru5, Pt2M6, PtM7, Pt2W4Ni2, PtAu6Hg and PtAu5Hg2. From a structural point of view, the clusters are complex with bicapped octahedrons of eight metal atoms prevailing. The shortest Pt-M bond distances (non-transition and transition) are 2.651(3) Å (M = Hg) and 2.624(1) Å (M = Os). The shortest Pt-Pt bond distance is 2.622(1) Å. These values are somewhat longer than those in the heteroheptanuclear clusters. Several relationships between the structural parameters were found, and are discussed and compared with the smaller heterometallic platinum clusters
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".