Industrial crude bioethanol dehydration to ethylene: Doping ZSM-5 to enhance selectivity and stability
Bibliographic record
Abstract
Although the conversion of bioethanol into light olefins is one of the most studied processes in biorefinery schemes, there is a need to develop materials more capable of operating at industrial conditions (WHSV > 20 h −1 crude bioethanol feed). Hence, in this study, we dehydrated crude bioethanol samples derived from sugarcane fermentation to produce ethylene over a series of H-ZSM-5 zeolites . Among them, H-ZSM-5 with different Si/Al ratios (26, 280, and 371) and doped with Ce and Cu were tested on the catalytic activity and stability. Accordingly, a 26 Si/Al ratio showed full conversion and ethylene selectivity at 300 °C with a WHSV of 30.2 h −1 . When doping the zeolites, a decrease in relative crystallinity and a higher amount of acid sites were observed, which affected the interaction with reactants. This interaction was deeply analyzed by the in-situ DRIFTS , which showed that ethanol adsorption is lower for doped zeolites, but the desorption rate is higher, showing higher stability over longer reaction times. Therefore, the H-ZSM-5 with a Si/Al ratio of 26 and doped with Ce maintained its activity and improved its selectivity over 140 h under more drastic conditions of WHSV (42.3 h −1 ). These results elucidate that Ce-doped H-ZSM-5 zeolites can improve stability and represent a starting point for large-scale crude bioethanol conversion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".