Mechanoenzymatic Depolymerization of Polyethylene Terephthalate in Moist Solids: Exploring the Roller Mill
Bibliographic record
Abstract
Plastic pollution has emerged as a critical global environmental challenge. Effective end-of-life management of plastics remains a pressing issue. Recent advances in enzymatic technology offer promising solutions for the closed-loop recycling of many plastics. For example, numerous enzymes capable of depolymerizing poly(ethylene terephthalate) (PET) into its building blocks have been identified. Notably, enzymatic hydrolysis conducted in moist-solid reaction mixtures is garnering interest as a more sustainable alternative to traditional dilute aqueous conditions. When combined with intermittent mechanical mixing, this approach, termed mechanoenzymology, can enhance enzyme performance, while addressing solubility issues and avoiding the need for substrate pretreatment. Despite these advances, current research in mechanoenzymology predominantly relies on laboratory-scale experiments using shaker mills. This study aims to broaden the scope of mechanoenzymatic transformations by exploring the use of a roller mill instead. Roller mills find widespread use industrially (e.g., in the mining and concrete industries). Utilizing commercial cutinase Novozym 51032 (abbreviated HiC), we investigated how varying milling conditions, moisture levels, and enzyme loadings impact the efficiency and scalability of PET depolymerization to terephthalic acid. The results demonstrate the scalability of enzymatic reactions in moist solids from 300 mg to 3 g and to 30 g scales at a solids loading of 40% w/w and daily milling at 300 rpm for 30 min. This study lays the groundwork for advancing PET recycling technologies on a larger scale.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".