Ruthenium‐Catalyzed Deconstruction of Polyolefins: A Strategy to Up‐cycle Waste Polyethylene to Value‐Added Alkene
Bibliographic record
Abstract
Synthesis of value-added products from post-consumer waste polyolefins is fascinating as well as challenging. Here we report ruthenium-catalyzed up-cycling of the polyethylene to long-chain alkene derivatives. The developed methodology mainly involves two steps i.e., dehydrogenation of polyethylene through hydrogen atom transfer and its metathesis using the HG-II catalyst. The dehydrogenation of polyethylene using ruthenium catalysis derived up to 3.38 %, of double bonds; with 90 % of the recovered polyolefin material. The obtained unsaturated polyethylene was subjected to cross-metathesis with ethylene using HG-II catalytic system. This resulted in the synthesis of predominantly dodecene (C12) derivatives, with 58 % selectivity, along with other derivatives of varying chain lengths. The overall reaction produced terminal and internal olefins in the ratio 1:0.8 respectively. The dehydrogenation of polyethylene and its deconstruction was confirmed by NMR spectroscopy, Gel Permeation Chromatography (GPC) and Differential Scanning Calorimetry (DSC). The origin of C12 selectivity has been demonstrated by control experiments. The scope of the methodology was extended to post-consumer waste polyethylene which gave high conversion to value-added dodecene derivatives as a major product.
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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".