Disordered, Sub-Nanometer Ru Structures on CeO<sub>2</sub> are Highly Efficient and Selective Catalysts in Polymer Upcycling by Hydrogenolysis
Bibliographic record
Abstract
Nondegradable polyolefin plastics pose severe environmental threats and thus demand efficient upcycling technologies. In this work, we discovered that low-loading (≤0.25 wt %) Ru/CeO 2 exhibits remarkable catalytic performance in the hydrogenolysis of polypropylene (PP), polyethylene (PE), and n -C 16 H 34 that is superior to high-loading (≥0.5 wt %) Ru/CeO 2 . They possess high PP conversion efficiency (sevenfold increase over current literature reports), low selectivity toward undesired CH 4, and good isomerization ability. In the low-loading range, the intrinsic activity of Ru in PP hydrogenolysis increases as the particle size decreases, opposite of the trend in the high-loading range. Detailed characterization revealed that the abrupt changes in catalytic behaviors coincide with Ru species transitioning from well-defined to highly disordered structures in the low-loading domain. The disordered Ru species were shown to be sub-nanometer in size and cationic. Mechanistically, the regioselectivity and the rate dependence on hydrogen pressure of C–C bond cleavage are different on low- and high-loading Ru/CeO 2, both explained by the higher coverage of adsorbed hydrogen (*H) on low-loading Ru/CeO 2 . This work reveals the remarkable catalytic performance of highly disordered, sub-nanometer, cationic Ru species in polyolefin hydrogenolysis, opening immense opportunities to develop effective, selective, and versatile catalysts for plastic upcycling.
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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".