Pretreatment of polyethylene terephthalate (PET) using physicochemical methods and their effects on biodegradation
Bibliographic record
Abstract
Abstract Recently, biodegradation has gained importance as a potential solution to alleviate pollution. This study dives into the physicochemical transformations of polyethylene terephthalate (PET) to enhance biodegradation efficiency. PET films were subjected to pretreatments, including UV irradiation, thermal oxidation, size reduction, and a combination of thermal oxidation and size-reduction pretreatments. These pretreated samples were then biodegraded using either an immobilized enzyme or the whole-cell Thermobifida fusca YX. The physicochemical effects of these treatments were evaluated through techniques such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, scanning electron microscopy (SEM), and weight-loss analysis. The findings revealed that UV irradiation caused repetitive cycles of photo-oxidation over 3 h, which impaired biodegradation due to increased crystallinity. Conversely, thermal oxidation improved biodegradation up to an optimal temperature of 80 °C. Higher temperatures were favorable for whole-cell biodegradation, while slightly lower temperatures (70–80 °C) were optimal for enzyme-mediated processes. A similar trend was observed for thermally oxidized size-reduced particles, with the smallest particle size exhibiting the highest biodegradation rates, 21.25 ± 0.24% with the immobilized enzyme and 16.61 ± 0.63% with whole cells. The study further demonstrated that all pretreatments primarily targeted the ester linkage, specifically the C = O and C–H bonds. Additionally, the effects of pretreatments were tested on chemical hydrolysis. Due to its inherently caustic nature, chemical hydrolysis did not require any pretreatment. These findings shed light on the interplay of physical and chemical factors influencing biodegradation, offering valuable insights into the importance of pretreatments for the biological hydrolysis of such polymers. Graphical Abstract
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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) | 0.001 | 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".