FeCl3-catalyzed Synthesis of Dehydrodiisoeugenol
Bibliographic record
Abstract
Dehydrodiisoeugenol (DDIE) synthesis has been performed by modifying a method recommended by Leopold with a different ratio of isoeugenol and FeCl3 (1.9:1). FeCl3 was chosen as catalyst due to its efficiency and environment-friendly property. This modification yielded 22.93 % of product. The product, a white crystalline form, was characterized using thin layer chromatography, melting point, UV, IR, HRMS, and NMR spectroscopy, as well as HPLC, employing pure DDIE as the standard. TLC chromatogram showed Rf 0.32 using n-hexane/ethyl acetate (8:2). The crystals melted at 138-139 oC, while its UV maximum was detected at l 273 nm. IR spectrum showed a specific broad O-H stretch at 3437.15 cm-1, C-H aromatic and C-H alkene at 3163.26 and 3024.38 cm-1, C-H alkyl stretch at 2951.09 and 2927.94 cm-1. An overtone peak of aromatic was detected at 2100 to 1700 cm-1. C-O peak was detected at 1126.43 cm-1. HPLC showed that this compound was eluted at 11.886 minutes after it was injected to a C18 column 250 x 4 mm using a mixture of methanol and double distilled water (73:27) for mobile phase. HRMS spectra predicted that the molecular structure is C20H22O4 as showed by abundance peak at m/z 327.1595 of [M+H]+. 1H-NMR and 13C-NMR indicated that the synthesized compound contains 13 types of proton and 20 types of carbon. Herein we reported that white needle-like crystals of DDIE using FeCl3 as catalyst had been synthesized, moreover the decreasing of the catalyst reduced the yield of the product.
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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) | 0.002 | 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".