Entropy Confinement Promotes Hydrogenolysis Activity for Polyethylene Upcycling
Bibliographic record
Abstract
Abstract Chemical upcycling that catalyzes waste plastics back to high‐purity chemicals holds great promise in end‐of‐life plastics valorization. One of the main challenges in this process is the thermodynamic limitations imposed by the high intrinsic entropy of polymer chains, which makes their adsorption on catalysts unfavorable and the transition state unstable. Here, we overcome this challenge by inducing the catalytic reaction inside mesoporous channels, which possess a strong confined ability to polymer chains, allowing for stabilization of the transition state. This approach involves the synthesis of p‐Ru/SBA catalysts, in which Ru nanoparticles are uniformly distributed within the channels of an SBA‐15 support, using a precise impregnation method. The unique design of the p‐Ru/SBA catalyst has demonstrated significant improvements in catalytic performance for the conversion of polyethylene into high‐value liquid fuels, particularly diesel. The catalyst achieved a high solid conversion rate of 1106 g ⋅ gRu−1 ⋅ h−1 at 230 °C. Comparatively, this catalytic activity is 4.9 times higher than that of a control catalyst, Ru/SiO2, and 14.0 times higher than that of a commercial catalyst, Ru/C, at 240 °C. This remarkable catalytic activity opens up immense opportunities for the chemical upcycling of waste plastics.
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".