Sustainable 3D-printed cellulose-based biocomposites and bio-nano-composites: Analysis of dielectric performances
Bibliographic record
Abstract
The development of cellulose-reinforced biomaterials appears to be an attractive approach for the production of sustainable materials with good mechanical properties. Although 3D printing of cellulose-reinforced biomaterials has become popular, there is very little feedback regarding their use in electrical insulation applications. This study aims to propose bio-nano-composites containing polylactic acid (PLA), microcrystalline (MCC) and nanocrystalline (NCC) cellulose by fused filament fabrication (FFF) for electrical insulation applications. The influence of the 3D printing process, cellulose content and filler size on dielectric properties was investigated. The addition of cellulosic fillers, and considering their high polarity, increased the dielectric constant (ε'), dielectric loss (ε''), as well as the AC electrical conductivity (σAC) of the composites. Cellulosic fillers also increased the crystallization rate of the materials. At equivalent content, the highest polarization potential were observed for NCC-based composites and were attributed to the nanofiller's better dispersion and available specific surface area. Finally, the 3D printing process affects all the measured properties, due to a combined effect (lower crystalline content and voids presence). The porosity rate was measured at 2.5 % for the neat PLA and increased progressively from 3.1 % to 5.8 % for the cellulose-based composites. These findings showed the benefits provided by the FFF technology in the production of cellulose-based biocomposites with good electrical insulation properties, while noting some needed feedback on the thermomechanical properties of such materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".