Glycerol dehydration to acrolein: Selectivity control over CsPW/Nb<sub>2</sub>O<sub>5</sub> catalyst
Bibliographic record
Abstract
The catalytic dehydration of glycerol to acrolein is a very attractive alternative to the propylene‐based process, due to the future exhaustion of fossil feedstocks and the increasing production of biomass‐based glycerol. This work aimed to study the selectivity control over Cs2.5H0.5PW12O40/Nb2O5 (CsPW‐Nb). The Brønsted and Lewis acid sites were characterized by FTIR to understand the catalytic mechanism. The effects of reaction temperature (260−360 °C), oxygen co‐feed ratio (0−0.175), and glycerol concentration (0.2−0.5 g/g) were studied to determine the optimum conditions for the production of acrolein. The loading of CsPW on Nb2O5 increased the Brønsted acidity and enhanced the acrolein dehydration route. Co‐feeding oxygen at an appropriate ratio significantly enhanced the selectivity of the acrolein dehydration route and slightly enhanced the selectivity of the acetol dehydration route by suppressing the glycerol oligomerization reaction. The selectivity to acrolein significantly decreased when the glycerol concentration increased from 0.2 to 0.5 g/g due to increased glycerol oligomerization reactions. The acrolein selectivity in dehydration of glycerol was affected by reaction temperature, oxygen co‐feeding, and glycerol concentration. The highest acrolein selectivity (76.5 %) was obtained at 320 °C, O2/N2/glycerol molar ratio of 1/5/0.16 (mol/mol), and glycerol concentration of 0.2 g/g.
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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 | 0.000 |
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 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".