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 Ni<sup>2+</sup>/Al<sup>3+</sup>/Ce<sup>3+</sup> ratios were facilely synthesized by the coprecipitation method under a constant pH value. The reaction parameters (e.g., different Ni<sup>2+</sup>/Al<sup>3+</sup>/Ce<sup>3+</sup> ratios, reaction time, temperature, amount and the stability of catalyst) were studied in details and it was found that the Ni<sup>2+</sup>/Al<sup>3+</sup>/Ce<sup>3+</sup> 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 Ni<sup>2+</sup>/Al<sup>3+</sup>/Ce<sup>3+</sup> 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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".