Scaled up epoxidation of terpenes in microemulsion
Why this work is in the frame
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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. • Terpene epoxidation in microemulsion scaled up to hundreds of milliliters. • Epoxidation of limonene, pinenes, terpinenes, myrcene and farnesene. • High yield of oxygen use maintained upon scaling up • CTAHS surfactant confirmed as appropriate. • Epoxidation by dioxirane in aqueous medium whitout organic solvent.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 it