Optimizing the Dielectric and Mechanical Performance of 3D-Printed Cellulose-Based Biocomposites and Bionanocomposites through Factorial Design for Electrical Insulation Application
Bibliographic record
Abstract
Materials for low-permittivity and electrical insulation applications need to be re-engineered to achieve sustainable development. To address this challenge, the proposed study focused on the dielectric and mechanical optimization of 3D-printed cellulose-based composites for electrical insulation applications. Two different fillers, microcrystalline cellulose (MCC) and nanocrystalline cellulose (NCC), were used to create biocomposites and bionanocomposites, respectively, blended into a polylactic acid (PLA) matrix. The effects of infill ratio, printing temperature, and filler content on dielectric and mechanical properties were measured using an incomplete L9 (3^3) factorial design. The findings showed that the infill ratio was the most significant factor influencing the properties tested, directly attributable to the increase in material availability for polarization and mechanical performance. The second most influential factor was the filler content, increasing the polarity of the tested composites and decreasing the toughness of the biocomposites and bionanocomposites. Finally, printing temperature had no significant effect. Results for the biocomposites at a 50% infill ratio, 200 °C printing temperature, and a weight content of MCC of 15% gave a 60% higher tensile-mode stiffness than neat PLA printed under the same conditions, while exhibiting lower dielectric properties than neat PLA printed with a 100% infill ratio. These results pave the way for new lightweight materials for electrical insulation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".