Printability and properties of 3D-printed poplar fiber/polylactic acid biocomposite
Bibliographic record
Abstract
To efficiently and economically utilize a wood-plastic biocomposite, an eco-friendly biocomposite was prepared using modified poplar fiber and polylactic acid (PLA) via 3D printing technology for the first time. First, the effects of poplar fiber (0, 1, 3, 5, 7, and 9%) on the mechanical and rheological properties of the printed biocomposites were investigated. Subsequently, the printing parameters, including printing temperature, speed, and layer thickness, were optimized to obtain the biocomposite with superior properties. Finally, four printing orientations were applied to the biocomposite based on the optimized printing parameters to study the effect of filament orientation on the properties of the biocomposite. Favorable printability and mechanical properties of the biocomposite were obtained at 5% poplar fiber. The optimal printing temperature of 220 °C, speed of 40 mm/s, and layer thickness of 0.2 mm were obtained to produce the desired mechanical properties of the biocomposite with the printing orientation in a longitudinal stripe. However, the printing parameters should be chosen according to the applications, where different physical and mechanical properties are needed to achieve efficient and economical utilization of the biocomposites.
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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".