Highly Selective One-Pot Synthesis of Benzoin Ether Compounds on Ni- AlCe-Hydrotalcite Catalysts
Bibliographic record
Abstract
In this study, benzoin methyl ether and benzoin ethyl ether were highly selectively synthesized in one-pot reaction using the reusable NiAlCe-hydrotalcite catalysts, where the benzoin ether compounds are traditionally obtained in two steps through condensation and etherification. The high crystallinity NiAlCe-hydrotalcites with different Ni2+/Al3+/Ce3+ ratios were facilely synthesized by the coprecipitation method under a constant pH value. The reaction parameters (e.g., different Ni2+/Al3+/Ce3+ ratios, reaction time, temperature, amount and the stability of catalyst) were studied in details and it was found that the Ni2+/Al3+/Ce3+ ratios, reaction temperature and the reaction medium play crucial influence on the catalytic activity and products distribution. 99.2% selectivity of benzoin methyl ether was achieved at 85.4% conversion of benzaldehyde and 98.3% selectivity was obtained at 63.7% conversion using a NiAlCe-hydrotalcite catalyst with the Ni2+/Al3+/Ce3+ ratio of 22:10:1, respectively. Besides, NiAlCe-hydrotalcite catalysts were easily recycled through a simple separation process and show high stability over three consecutive runs.
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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 | 0.001 |
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".