Photothermal catalysis of waste plastics into propionic acid and hydrogen via Ni single-atom site isolation effect
Bibliographic record
Abstract
Currently, catalytic recycling of polyethylene (PE) into high-value chemicals using solar energy often faces poor product selectivity and low efficiency. This is mainly due to the difficulty in effectively controlling the intermediates during PE photoreforming and the long-standing challenge of inefficient charge dynamics. Here, we present a solar-driven photothermal catalytic approach for the selective conversion of PE waste into propionic acid and hydrogen under ambient conditions. Atomically dispersed Ni sites supported on CeO 2 (Ni SA /CeO 2 ) achieve a propionic acid yield of 331 μmol h –1 with 94.8% selectivity in the photothermal reaction. This performance is 1.6 times higher than that of catalysts supported by Ni clusters (Ni NP /CeO 2 ). Additionally, Ni SA /CeO 2 exhibits a hydrogen yield of 0.23 mmol h –1 with stable long-term performance. Mechanistic studies reveal that single Ni atoms form linear coordination with oxygen atoms in CeO 2 , introducing unoccupied mid-gap states that effectively capture hot electrons and enhance the photothermal effect through local hotspot formation. In contrast, Ni clusters suffer from inefficient heat accumulation due to multistep phonon scattering. Furthermore, site isolation of Ni single atoms spatially separates the reaction intermediates and suppresses dimerization of the key intermediate COOHCH 2 CH 2 *, thereby greatly improving the selectivity for propionic acid. In contrast, closely packed Ni cluster sites promote intermediate coupling and the formation of undesirable byproducts, reducing selectivity. This work provides mechanistic insights into the advantages of atomic-scale catalyst design for selective chemical transformations.
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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".