Effect of Biobased SiO2 on the Morphological, Thermal, Mechanical, Rheological, and Permeability Properties of PLLA/PEG/SiO2 Biocomposites
Bibliographic record
Abstract
Nowadays, companies and researchers are concerned about the negative consequences of using synthetic polymers and direct their efforts to create new alternatives such as biocomposites. This study investigated the effect of biobased SiO2 on the properties of poly(L-lactic acid)/SiO2 (PLLA/SiO2) and poly(L-lactic acid)/SiO2/poly(ethylene glycol) (PLLA/SiO2/PEG) composites. The SiO2 was obtained from rice husk incineration and mixed with PLLA at various concentrations (5, 10, and 15 wt.%) via melt extrusion before compression molding. Furthermore, PLLA/SiO2/PEG composites with various PEG concentrations (0, 3, 5, and 10 wt.%) with 10 wt.% SiO2 were produced. The sample morphology was studied by scanning electron microscopy (SEM) to analyze the dispersion/adhesion of SiO2 in the polymer matrix and differential scanning calorimetry (DSC) was used under isothermal and non-isothermal conditions to study the thermal properties of the samples, which was complemented by thermal stability study using thermogravimetric analysis (TGA). Rheological analysis was performed to investigate the viscoelastic behavior of the composites in the melt state. At the same time, tensile mechanical properties were obtained at room temperature to determine their properties in the solid state. DSC and X-ray diffraction analysis (XRD) were combined to determine the crystalline state of the samples. Finally, gas permeation measurements were performed using a variable pressure (constant volume) method to analyze the permeability of different gases (CO2, CH4, O2, and H2). The results showed that SiO2 decreased the PLLA chain mobility, slowing the crystallization process and lowering the gas permeability while increasing Young’s modulus, thermal stability, and viscosity. However, PEG addition increased the crystallization rate compared to the neat PLLA (+40%), and its elongation at break (+26%), leading to more flexible/ductile samples. Due to improved silica dispersion and PLLA chain mobility, the material’s viscosity and gas permeability (+50%) were also improved with PEG addition. This research uses material considered as waste to improve the properties of PLA, obtaining a material with the potential to be used for packaging.
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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".