Polyester (PET) Degradation in Mild-Alkaline Solutions Assisted by Ultrasonication and UV-Activated Metal Oxides
Bibliographic record
Abstract
The exponential and continuous growth in the production and consumption of plastics, particularly polyethylene terephthalate (PET), due to their versatile applicability, has led to the accumulation of persistent plastic waste in the environment. This has created an urgent need to develop more sustainable and efficient recycling technologies. Although several recycling methods, such as mechanical and chemical recycling, have been implemented on an industrial scale, existing technologies still do not fully recover PET to its original quality or rely heavily on hazardous chemicals at elevated operating conditions. In this study, we explored a greener approach for PET depolymerization through hydrolysis under milder conditions, enhanced by mechanochemical degradation via ultrasonication, and integrated with UV-assisted treatment using five heterogeneous catalysts (ZnO, ZnFe₂O₄, Fe₂O₃, Y₂O₃, and SrO). The results of this study proved the significance of the mechanochemical effect of ultrasonication, which enhanced PET degradation by promoting chain scission, achieving up to 21.5% PET conversion and 19.0% terephthalic acid (TPA) yield in 1M NaOH hydrolysis. Among the catalysts tested under 0.1M NaOH hydrolysis, SrO exhibited the highest catalytic performance due to the formation of Sr(OH)2, which provides additional hydroxide ions for ester bond cleavage. However, excessive SrO loading led to SrSO₄ precipitation during acidification, requiring additional purification of the TPA 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) | 0.000 | 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".