Identification of ruthenium chloride complexes in the coordination precursor <i>Ruthenium Blue</i> and its oxidation products
Bibliographic record
Abstract
The Ru xCl y compounds that make up the widely used Ru coordination precursor, Ruthenium Blue, have been difficult to identify due to fast air-driven oxidation. To explore the coordination between Ru and aminated polysaccharides, the reaction between reducing glucosamine (GlcN) and Ruthenium Blue was investigated. A blue vibrant solution, indistinguishable from that of Ruthenium Blue, was obtained after reflux of GlcN/RuCl3:3H2O in ethanol, but unlike Ruthenium Blue, the blue colour of the reaction product persisted after prolonged exposure to air. Mass spectrometry (MALDI-ToF-MS) of the blue solution did not detect GlcN-Ru ligands. Instead, isotope patterns for singly charged Ru xCl y clusters were observed for [RuCl4]−, [Ru2Cl6]−, [Ru2Cl7]−, [Ru3Cl8]−, [Ru3Cl9]−, [Ru4Cl11]−, and [Ru5Cl12]−. Extended exposure to air led to a colour change of the blue solution to green and finally to yellow. The MS spectrum of the green solution showed lower amounts of the higher order [Ru4Cl11]− and [Ru5Cl12]−, and that of the yellow solution displayed mostly [RuCl4]−. The compositions identified by MS allowed the determination of structures and corresponding electronic excitations by ab initio calculations using density functional theory. The excitation energies were matched to physically perceived colours with CIE1931 colour matching functions, with a noteworthy association of [Ru4Cl11]− to blue. We propose that the presence of the reducing monosaccharide slowed the oxidation of the Ru xCl y clusters in the blue solution and that the structures identified are, at least in part, constituents of Ruthenium Blue. These data may also be useful in the development of Ru-based reagents for catalysis and radiotherapy applications.
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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 | 0.000 | 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".