Effect of CeO2 morphology and Ru impregnation method on CH4 selectivity reduction in polyethylene waste conversion to liquid fuels and lubricants
Bibliographic record
Abstract
• Ru dispersion influenced by support morphology and impregnation method. • Smaller Ru particles on CeO 2 show reduced CH 4 selectivity. • Defect site on CeO 2 (oxygen vacancies) serve as hydrogen reservoir sites. • Dissociated H 2 can spillover to intra and interparticle CeO 2 . • Enhanced hydrogen reservoir boosts hydrogenolysis rate and lowers CH 4 selectivity. Hydrogenolysis of polyolefins offers a sustainable pathway by giving plastic waste a second life as valuable resources, transforming it into fuels and lubricants. Supported Ru catalysts have shown potential for depolymerizing polyolefins under mild conditions; however, depolymerization via terminal C-C bond cleavage can lead to the excessive formation of CH 4 . This study investigated the effects of CeO 2 morphology and Ru impregnation method on the suppression of CH 4 formation and increasing liquid and wax proportions (C 5 -C 41 ) in the production of liquid fuels and lubricants through polyethylene hydrogenolysis. Controlling the morphology of CeO 2 and impregnating Ru via electrostatic adsorption can enhance the formation of oxygen vacancies and strengthen the interaction between Ru and CeO 2 . The presence of defects in CeO 2 also correlated with smaller particle size of Ru and a higher hydrogen reservoir site, which effectively suppress CH 4 formation.
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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 | 0.000 |
| 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) | 0.000 | 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".