Origin of active sites on silica-magnesia catalysts and control of reactive environment in the one-step ethanol-to-butadiene process
Bibliographic record
Abstract
Abstract Wet-kneaded silica–magnesia is a benchmark catalyst for the one–step ethanol-to-butadiene Lebedev process. Magnesium silicates, formed during wet-kneading, have been proposed as active sites responsible for butadiene formation, and their catalytic performance has been mainly explained by the variations in the ratio of acid and base sites. While the Lebedev process was developed in the 1930s, However, a detailed insight into how the peculiar, yet essential wet-kneading synthesis leads to the generation, location, and catalytic role of magnesium silicates has not been fully established. Here, we demonstrate that magnesium silicates formation occurs via dissolution of Si and Mg subunits from SiO2 and Mg(OH)2 precursors, initiated by the alkaline pH of the aqueous wet-kneading medium, followed by cross-deposition of the dissolved species on the precursor surfaces. Building on these new insights, two individual model systems (Mg/SiO2 and Si/MgO) were synthesized, representative of the constituents of the wet-kneaded silica–magnesia catalyst, by selective dissolution/deposition induced by pH alteration of the aqueous wet-kneading medium. Using these model catalysts, we demonstrate that the location of the magnesium silicates (i.e., Mg on SiO2 or Si on MgO) governs not only their chemical nature but also the ethanol adsorption configuration, which ultimately cause the catalyst material to be selective mainly for ethylene or butadiene. We demonstrate close proximity at the particle level of the of acid and basic sites is a prerequisite to promote the butadiene formation. The insights gained from the new structure–performance relationships that correlate catalytic activity with types and nature of magnesium silicates can offer new possibilities for the development of next generation Lebedev catalysts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".