Partial and Total Solvent-Free Limonene’s Hydrogenation: Metals, Supports, Pressure, and Water Effects
Bibliographic record
Abstract
Bio-based solvents menthene and menthane were obtained through limonene’s partial and total hydrogenation under various catalytic conditions. Heterogeneous catalysts based on different active metals and supports (carbon, alumina, and silica) were systematically tested for solvent-free total and partial hydrogenation of limonene under high and low hydrogen pressure. Influences of these catalysts on the formation of menthene, menthane, and cymene, a dehydrogenated product, were determined. The impact of water addition on the conversion and selectivity of the catalysts was also investigated. Amongst all tested catalysts, Rh/Alumina which was never tested for total and partial hydrogenation of limonene was the most effective as 1-menthene was quantitatively produced at low pressure (0.275 MPa) while menthane was mostly obtained at a higher pressure (2.75 MPa). Water addition on Rh/Alumina favoured menthene production even at high pressure. To propose menthane, menthene, and menthane/menthene mixture as an alternative to fossil-based solvents such as n-hexane for the extraction of natural products, β-carotene, vanillin, and rosmarinic acid solubilizations have been investigated. If a modeling approach using COSMO-RS software predicted a comparable solubilization of these 3 compounds for the 3 solvents, experimental assays revealed that menthene solubilizes β-carotene, vanillin, and rosmarinic acid three to five times better than n-hexane.
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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".