Dielectric and viscoelastic properties of 3D-printed biobased materials
Bibliographic record
Abstract
This study aimed to develop new biobased materials containing plasticized cellulose acetate (CA) by Fused Filament Fabrication (FFF). We investigated the influence of CA on the viscoelastic/dielectric properties and the influence of 3D printing on the dielectric properties of the polymer blends. A microstructural analysis showed that the blends had a strong heterogeneous morphology, as the two phases formed a fibrillar and lamellar structure. CA strongly increased the blend viscosity at 175 °C, multiplying by 100 times the complex viscosity between neat PLA and the blend containing 40% of CA by weight (CA-40). The addition of CA to the blends increased the dielectric constant (ε') and dielectric loss (ε''), as well as the alternative current electrical conductivity (σAC). The dielectric constant of CA (ε'CA) was proportional to its percentage in the blend, showing a behavior analogous to a rule of mixture. Moreover, the activation energy of the electrical conductivity (σDC) measured at T > 124 °C decreased with increasing CA content and was associated with the enhancement of the ionic conductivity provided by the CA and its plasticizer. A decrease in the α-relaxation temperature and the associated activation energy of the PLA was also linked to the presence of the CA plasticizer. Finally, 3D printing greatly decreased both ε', ε'' and σAC due to the internal voids induced by the 3D printing process. The porosity was measured at 12% for neat PLA and between 20% and 25% for the blends. These results showed the advantage provided by FFF technology in the production of PLA:CA blends with controlled dielectric properties, thereby favoring the use of these new materials in key dielectric areas.
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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 | 0.000 | 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) | 0.000 | 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".