Application and Synthesis of [Ru(CH3P(CH2PPh2)3)(CH3CN)3](OTf-)3 as a Catalyst for the Selective Hydrodeoxygenation of Biomass-Derived Substrates in Aqueous Acidic Media
Bibliographic record
Abstract
The goal to replace chemicals from fossil resources and increased environmental consciousness has focused research on the development of green processes to obtain high value and low volume chemicals from biomass. The use of solvents such as water has necessitated the development of soluble and robust homogeneous catalysts to maintain activity in aqueous conditions. This work outlines the synthesis of a molecular ruthenium pre-catalyst with the cationic tridentate hetero-triphos ligand [CH3P(CH2PPh2)3](OTf-), first described by Guenther et al. in 2010. Comparison of bond distances is performed between previously synthesized triphos and hetero-triphos containing complexes to the novel complexes presented in this work, to identify possible predictors of reactivity. Furfuryl acetate, a non-edible biomass derived platform chemical, was chosen as the substrate to evaluate the catalytic activity of [Ru(CH3P(CH2PPh2)3)(CH3CN)3](OTf-)3 as this will also allow for comparison of activities between previous works. Valuable products were identified and their yields quantified, with the best reaction conditions (175 °C, 0.1 mol% catalyst, 250 mM substrate) giving 2-methylfuran (54%), 1,4-pentanediol (32%) and cyclopentanol (5%). The saturation concentration of the complex in D2O was found to be 0.001 mM, indicating that high solubility in water will continue to be a challenge for the triphos series of catalysts.
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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".