From Corn to Cassava: Unveiling PLA Origins for Sustainable 3D Printing
Bibliographic record
Abstract
The focus of this paper is to review the polylactic acid (PLA) sourcing for 3D printing and investigating with a specific emphasis on corn starch, sugarcane, and cassava starch. PLA is recognized for its biodegradable nature and versatility as a thermoplastic, has witnessed a notable evolution in the global context of material selection for 3D printing. While regions such as the United States and Canada have traditionally derived PLA from corn starch, there is a growing trend in Asia where cassava starch have emerged as prominent alternatives. This study seeks to discover the complexities of the of PLA origins, looking into the sustainability considerations that contribute to the selection of source materials. By shedding light on the diverse trajectories of PLA sourcing, this study provides valuable insights into the ever-changing dynamics of material preferences for 3D printing on a worldwide scale. Moreover, the understandings generated through this study are composed to play a pivotal role in shaping the trajectory of future practices in additive manufacturing. As the industry continues to evolve and grapple with the imperative of environmental responsibility, a nuanced understanding of the sustainability dimensions of PLA sourcing becomes a compass guiding researchers, practitioners, and manufacturers toward ecologically sound choices. Ultimately, the study serves as a valuable resource, empowering participants to navigate the complex landscape of PLA-based 3D printing with a thorough judgement on sustainability, thereby fostering a more environmentally responsible future for additive manufacturing.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".