Platinum organometallic complexes: classification and analysis of crystallographic and structural data of tri- and oligomeric complexes
Bibliographic record
Abstract
Abstract This review covers almost 100 organoplatinum complexes: trimers (40 examples), tetramers (40 examples), pentamers (4 examples), hexamers (5 examples), nona- and oligomers (8 examples). Platinum is predominantly found in the oxidation states +2 and +4. A number of coordination geometries are observed, the most common being essentially square planar, especially with Pt(II), and distorted octahedral, especially with Pt(IV). The most common ligands are methyl, carbonyl, PX3 and bis(diphenylphosphine)methane. Relationships between the Pt-Pt distances, Pt-X-Pt bridge angles, Pt-L bond distances and covalent radii of coordinated atoms are discussed. The mean Pt-Pt bond distance elongates in the order of nuclearity: 269.0 pm (trimers)<270.5 pm (tetramers)<271.5 pm (dimers)<278.0 pm (oligomers). A comprehensive brief discussion on over 1600 organoplatinum complexes and over 2500 platinum coordination complexes is given. These complexes prefer to crystallize in monoclinic (53%) and triclinic (27%) crystal classes. About l0% of these 4100 plus complexes exist as isomers. It is observed that these isomers are more often stereoisomers than structural isomers and that distortion isomerism is surprisingly more common than the better known cis-trans isomerism, especially in the chemistry of Pt(II) complexes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".