Ultra‐Narrow Alkane Product Distribution in Polyethylene Waste Hydrocracking by Zeolite Micro‐Mesopore Diffusion Optimization
Bibliographic record
Abstract
Abstract Plastic pollution poses a pressing environmental challenge in modern society. Chemical catalytic conversion has emerged as a promising solution for upgrading waste plastics into valuable liquid alkanes and other high value products. However, the current methods yield mixed products with a wide carbon distribution. To address this challenge, we present a bifunctional catalytic system consisting of β zeolite mixed hierarchical Pt@Hie‐TS‐1, designed for the conversion of low‐density polyethylene (LDPE) into liquid alkanes. This system achieves a 94.0 % yield of liquid alkane, with 84.8 % of C 5 −C 7 light alkanes. Combined with in situ FTIR and molecular dynamics simulation, the shape‐selective mechanisms is elucidated, which ensures that only olefins of the appropriate size can diffuse to the encapsulated Pt sites within the zeolite for hydrogenation, resulting in an ultra‐narrow product distribution. Furthermore, by optimizing the micro‐mesopores of Pt@Hie‐TS‐1, the scaling relationship between the pore structure and the conversion/selectivity is identified. The rapid diffusion of olefins within these micro‐mesopores significantly enhances the catalytic efficiency. Our findings contribute to the design of efficient catalysts for plastic waste valorization.
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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".