Scaled up epoxidation of terpenes in microemulsion
Bibliographic record
Abstract
The dioxirane epoxidation was traditionally carried out in a two-phase medium: an organic solvent to dilute the olefin to be epoxidised and an aqueous medium in which the peroxymonosulphate oxidant is dissolved. This phase incompatibility generally requires a large quantity of oxidant and oxygen yield does not exceed 25 %. Despite the major advantages of this technique, it is still only used at laboratory scale. The aim of this study is to illustrate a dioxirane epoxidation ability to produce terpene epoxides at the scale of hundreds of milliliters. The technique employs an acetone catalyst that reacts with oxone® to in-situ form dimethyldioxirane, which epoxidizes the terpene. Oxone® is an aqueous oxidant. To remedy its incompatibility with hydrophobic terpene the use of a surfactant at concentration above its critical micelle concentration (cmc) enabled the terpene to be highly dispersed in the aqueous medium in absence of any additional organic solvent by forming a microemulsion, thus improving interface reaction rate and reducing oxygen loss. At 25°C and for a t r of 90 min, several hundred milliliters of limonene, α-pinene, β-pinene, α-terpinene, γ-terpinene, β-myrcene and farnesene were easily epoxidized with both conversion and selectivity of almost 100 %. Oxygen yields of up to 80 % were achieved, against 29 % in absence of microemulsion. The high selectivity of epoxides, the solvent-free aqueous reaction, the ambient conditions, the simple acetone used as catalyst and the easy separation of epoxides from the aqueous medium make this innovative process more appropriate for easy scale-up.
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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 | 0.001 |
| 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".