Mechanoenzymatic Depolymerization of Highly Crystalline Polyethylene Naphthalate under Moist-Solid Conditions
Bibliographic record
Abstract
Plastics have dramatically improved our quality of life. Their lightweight, chemical resistance, and barrier properties are some of the advantages that make them superior to other materials, delivering health and energy-saving benefits among others. However, plastic recycling technologies are urgently needed to address the overwhelming accumulation of plastics in the environment. Here, we report a strategy that combines enzymatic catalysis and mechanical mixing, otherwise known as mechanoenzymology, with moist-solid reaction conditions to enable the clean hydrolysis of the recalcitrant plastic polyethylene naphthalate (PEN) to its building block 2,6-naphthalenedicarboxylic acid (2,6-NDA). Using a commercial variant of Humicola insolens cutinase (HiC, Novozym 51032) added in batches allowed us to achieve 56% yield of 2,6-NDA from high crystallinity ( X c = 41%) PEN. Remarkably, comparable reactions under standard aqueous conditions afforded 30 times less product. The high yield obtained together with the lack of increase in plastic crystallinity ( X c ) over the course of the reaction suggest that under moist-solid reaction conditions, both the amorphous and crystalline regions of the plastic are depolymerized. Preliminary mechanistic and kinetic studies are also presented to pave the way for future optimization.
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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".