Pyrolysis of bamboo over <scp>Ce/Fe</scp> composite metal oxide catalyst to enhance the production of hydrocarbons and ketonic hydrocarbon precursors
Bibliographic record
Abstract
Abstract To obtain high‐value bio‐oil, pure ceria (CeO 2 ) and a series of Ce/Fe composite metal oxides were synthesized via precipitation method and were used to enhance hydrocarbons and ketones production during catalytic pyrolysis of bamboo sawdust. The characterization results were comprehensively analyzed and revealed that doping CeO 2 with Fe promoted the formation of a solid solution structure, which further increased the surface area and number of oxygen vacancies of the catalyst for deoxygenation. Experimental consequences demonstrated that, compared to non‐catalytic trial, the catalytic pyrolysis over CeO 2 generated lower amounts of acids and aldehydes, and enhanced the conversion of large oxygenates to monofunctional hydrocarbon precursors via decarboxylation, deoxidation, and ketonization. The concentrations of hydrocarbons and ketones obtained over Ce/Fe catalysts were significantly higher than those obtained over CeO 2 , and that was attributed to the higher surface area and oxygen storage capacity of Ce/Fe catalysts. Particularly, the composite catalyst with the Ce/Fe molar ratio of 4 (Ce 0.8 Fe 0.2 ) presented the most optimal deoxidation capacity in this study. The relative concentration of hydrocarbons generated over Ce 0.8 Fe 0.2 was the highest, and monocyclic aromatics and short‐chain aliphatic hydrocarbons accounted for 47.13% and 29.72%, respectively, of the total hydrocarbons. Simultaneously, the amount of ketones, the main hydrocarbon precursors, obtained over Ce 0.8 Fe 0.2 was significantly higher than that obtained over CeO 2 , and the fraction of linear and cyclic ketones of the total ketones increased from 45.96% for the non‐catalytic pyrolysis to 97.57%. This further confirmed that the mesoporous Ce/Fe composite catalysts efficiently catalyzed the aldol condensation and ketonization reactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".