Photocatalytic Conversion of Polyester‐Derived Alcohol into Value‐Added Chemicals by Engineering Atomically Dispersed Pd Catalyst
Bibliographic record
Abstract
Abstract Photoreforming presents a promising strategy for upcycling waste polyester‐derived alcohol into valuable chemicals. However, it remains a great challenge due to its low performance and unsatisfactory selectivity toward high‐value C2 products. Here, we report the highly efficient and selective conversion of ethylene glycol (EG, a monomer of polyethylene terephthalate (PET)) to glycolaldehyde using atomically dispersed Pd species supported on TiO2 catalyst. A glycolaldehyde production rate of 5072 μmol gcat−1 h−1 with a selectivity of 90.0 % and long‐term durability can be achieved. Experimental and theoretical results show that Pd single atoms can enhance the photocatalytic activity by enriching the photogenerated holes, which are the dominant species for the selective oxidation of EG to glycolaldehyde. More importantly, the adsorption of EG molecules on the catalysts is significantly promoted, which is subsequently transformed into RO⋅ radicals, a crucial intermediate in producing glycolaldehyde. Additionally, Pd single atoms on TiO2 enable the reduction of the glycolaldehyde desorption barrier, thereby facilitating high selectivity and inhibiting further oxidation to C1 products. This work provides new insights into the photocatalytic conversion of polyester wastes by atomic engineering.
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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".