Recycling Commodity Plastic Waste for Vat Photopolymerization 3D Printing of High-Performance Polymeric Composites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Recycling plastic waste is crucial for reducing environmental harm and conserving resources. However, current methods face challenges, such as the need to sort different polymers, limited compatibility across plastic types, and reliance on hazardous chemicals in chemical recycling. This study introduces a sustainable additive manufacturing approach by repurposing commonly discarded thermoplastics, including polylactic acid (PLA), polyamide (PA), polypropylene (PP), polyethylene terephthalate (PET), and 3D-printed thermosets, as feedstock for digital light processing (DLP) 3D printing. Using cryogenic milling, these polymers are transformed into fine powders and incorporated into a photocurable resin to create polymeric composites. The addition of solid plastic particles presents two main challenges: increased resin viscosity and UV light blocking. These issues affect both printability and mechanical performance. To address viscosity, a heated vat system maintains the resin at ∼55 °C, improving flow without compromising the process. Additionally, UV light is blocked during photopolymerization, leading to incomplete curing and a 50% reduction in modulus compared to neat resin. A dual-curing approach mitigates this by combining UV-curing via a photoinitiator with thermal annealing via a thermal initiator, ensuring full polymerization and restoring mechanical strength. This strategy yields an ∼250% increase in modulus for high-loading samples, aligning with theoretical predictions. The study demonstrates broad applicability across various powder–resin combinations, highlighting its adaptability in diverse material contexts. Overall, this work establishes a pathway for incorporating a wide range of recycled plastics into high-performance 3D-printed composites, advancing both the sustainability and the functional potential of additive manufacturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".