Stable Interfacial Ruthenium Species for Highly Efficient Polyolefin Upcycling
Bibliographic record
Abstract
The present polyolefin hydrogenolysis recycling cases acknowledge that zerovalent Ru exhibits high catalytic activity. A pivotal rationale behind this assertion lies in the propensity of the majority of Ru species to undergo reduction to zerovalent Ru within the hydrogenolysis milieu. Nonetheless, the suitability of zerovalent Ru as an optimal structural configuration for accommodating multiple elementary reactions remains ambiguous. Here, we have constructed stable Ru 0 –Ru δ+ complex species, even under reaction conditions, through surface ligand engineering of commercially available Ru/C catalysts. Our findings unequivocally demonstrate that surface-ligated Ru species can be stabilized in the form of a Ru δ+ state, which, in turn, engenders a perturbation of the σ bond electron distribution within the polyolefin carbon chain, ultimately boosting the rate-determining step of C–C scission. The optimized catalysts reach a solid conversion rate of 609 g·g Ru –1 ·h –1 for polyethylene. This achievement represents a 4.18-fold enhancement relative to the pristine Ru/C catalyst while concurrently preserving a remarkable 94% selectivity toward valued liquid alkanes. Of utmost significance, this surface ligand engineering can be extended to the gentle mixing of catalysts in ligand solution at room temperature, thus rendering it amenable for swift integration into industrial processes involving polyolefin degradation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".