Highly selective solvent-free hydrogenation of pinenes to added-value cis-pinane
Bibliographic record
Abstract
Heterogeneous catalysts based on different active metals (Pd, Pt, Ru, and Rh) and supports (carbon and alumina) were systematically tested for hydrogenation of pinenes under various reaction conditions including heating and/or sonication. Among all, Ru provided some of the best results. After sonication, Ru/C was found highly active and selective for the reduction of α-pinene alone into cis -pinane (99% selectivity at 100% conversion). However, in the absence of sonication the same catalyst was completely inactive. Alumina appeared to be a support with a beneficial chemical effect on the activity of the Ru. Indeed, Ru/Al 2 O 3 was found very active and selective for hydrogenation of both α- and β-pinene into cis -pinane (99–100% selectivity at 100% conversion) under mild reaction conditions (room temperature, 400 Psi H 2 ). The selectivity toward cis -pinane decreased in the following order: Ru > Rh > Pt > Pd. An extremely low leaching rate of Ru and other metals determined by inductively coupled plasma mass spectrometry confirmed the heterogeneous nature of this catalytic solvent-free hydrogenation. Ru/C was successfully recycled seven times with no decline in activity and selectivity, whereas Ru/Al 2 O 3 could be used only once to convert pinenes to cis -pinane. Solubilization prediction tests of some natural products have revealed that pinane compares very well with n -hexane and may be an alternative to this fossil-based solvent. When compared experimentally with n -hexane, cis -pinane solubilized 42, two, and three times more of β-carotene, vanillin, and rosmarinic acid, respectively.
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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".