3D printing in upcycling plastic and biomass waste to sustainable polymer blends and composites: A review
Bibliographic record
Abstract
Mishandling of waste plastics and biomasses is a major global concern. Every year, around 380 million tons of plastic are produced, with only 9% being recycled, leading to widespread pollution. Similarly, waste biomass generation from agricultural and forestry sectors accounts for 140 billion metric tons, in addition to 2.01 billion tons from municipal solid waste. This review paper addresses the gap regarding the integration of 3D printing, upcycling of recycled plastics, and the utilization of waste biomass in sustainable composites. 3D printed parts from recycled plastic have shown comparable mechanical properties compared to virgin materials, which have been further improved by the addition of waste biomass-derived fillers. The paper acknowledges that different printing parameters have substantial influence on the strength, ductility, crystallinity, and dimensional accuracy of printed parts. Therefore, optimizing these parameters becomes crucial for achieving improved mechanical performance. Moreover, incorporating reinforcing agents, stabilizers, chain extenders, compatibilizers, and surface modifiers in plastic recycling and 3D printing presents an excellent opportunity to enhance mechanical properties, thermal stability, adhesion, and dimensional stability. Additionally, the review identifies research gaps and proposes the integration of machine learning and artificial intelligence for enhanced process control and material development, further expanding the possibilities in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".