Sustainable composites from microcrystalline cellulose and cellulose acetate: 3D printing and performance optimization
Bibliographic record
Abstract
• One of the first attempts to investigate plasticized cellulose acetate blended with microcrystalline cellulose for 3D printing. • Printing parameters optimized using Taguchi design, Grey Relational Analysis, and Principal Component Analysis. • Best mechanical performance achieved at 230 °C nozzle temperature, 1800 mm/min speed, 100 % infill, 0° raster angle, and 0.15 mm layer height. • 3D-printed samples showed 37 % higher impact strength than injection-molded. • 3D printed finger splint prototype demonstrated the material suitability for medical devices. Novel green composites were developed using microcrystalline cellulose (MCC) and plasticized cellulose acetate (pCA) to assess their viability for application in additive manufacturing (AM), specifically fused filament fabrication (FFF). This study represents one of the first attempts to fabricate and optimize a sustainable MCC-pCA composite for use as a 3D printing filament. The Taguchi L27 experimental design was employed to optimize five critical FFF parameters, namely nozzle temperature, printing speed, infill density, raster angle, and layer height, with the objective of maximizing mechanical performance. Optimal printing parameters were determined to be a nozzle temperature of 230 °C, a printing speed of 1800 mm/min, an infill density of 100 %, a raster angle of 0°, and a layer height of 0.15 mm. Under these conditions, the 3D-printed samples exhibited mechanical properties comparable to those of injection-molded counterparts, with a 37 % increase in impact strength. The coefficient of linear thermal expansion (CLTE) of the optimized 3D-printed sample was 89.36 μm/m °C (perpendicular) and 65.39 μm/m °C (parallel), demonstrating lower thermal expansion than injection-molded counterparts (108.65 μm/m °C and 47.06 μm/m °C, respectively). Furthermore, the heat deflection temperature (HDT) of the optimized 3D-printed sample was 92.18 °C, surpassing that of injection-molded samples (69.59 °C), indicating superior thermal resistance in the 3D-printed part. As a proof-of-concept, a 3D printed finger splint was fabricated using the optimized parameters, showcasing the potential of this sustainable composite for biomedical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".