Poly(Lactic Acid) (PLA)-Based Nanocomposites: Impact of Vermiculite, Silver, and Graphene Oxide on Thermal Stability, Isothermal Crystallization, and Local Mechanical Behavior
Bibliographic record
Abstract
The structural, thermal, and mechanical properties of unreinforced and reinforced polylactic acid (PLA) were investigated. The PLA was a biopolymer that was reinforced with four fillers (i.e., graphene oxide (GO) and silver (Ag); vermiculite (VMT) and silver (Ag); and two organically modified vermiculites). The processing technique for the production of the composite materials were carefully planned. The PLA nanocomposites were investigated by examining their morphological aspects, changes in PLA phases and transitions and, most importantly, the effect on certain final properties. X-ray diffraction and differential scanning calorimetry (DSC) analysis indicated that the sample was completely amorphous. Thermogravimetric analysis (TGA) results indicated that the presence of reinforcing particles in the PLA matrix did not affect the thermal degradation of these composites. Furthermore, the local mechanical properties were investigated using the microindentation method to evaluate the effect of different nanofillers. Scanning electron microscopy (SEM) and a VHX-500 optical digital microscope (Keyence International, Mechelen, Belgium) were also used to examine the surface morphology of the PLA polymer composites. These results can help to select suitable fillers to enhance the PLA performance of biopolymers.
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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) | 0.000 | 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 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".