Microstructure Development and Its Effect on the Properties of Melt‐Processed Biodegradable Polylactide/Poly(ε‐Caprolactone) Blends
Bibliographic record
Abstract
The relationship between structure and properties in polymeric materials explores how variations in polymer blend composition affect their microstructure and alter rheological, thermal and mechanical characteristics. This study focuses on polylactide (PLA)/poly(ε‐caprolactone) (PCL) blend, which is selected for its biodegradable and biocompatible properties, enabling applications ranging from packaging to biomedical fields. PLA/PCL blends with different PCL loadings were processed in a twin‐screw extruder. We assessed the correlation between blend microstructure and properties to analyse mechanical performance under various loading conditions. The blend with 10 wt% PCL exhibited droplet‐matrix morphology with well‐dispersed PCL particles, strong interfacial adhesion and notable crystallinity, as shown through scanning electron microscopy (SEM), rheological analysis, dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The high PLA content, excellent dispersion and significant crystallinity resulted in elevated tensile strength and toughness, as well as reduced brittleness in tests. However, the material exhibited low notched Charpy impact strength. This indicates that it can deform under tensile and repetitive loads, yet exhibits poor resilience to sudden impacts under notched conditions. The droplet‐matrix morphology is validated as the experimental tensile modulus aligns with Takayanagi model predictions. These findings emphasise the importance of blend microstructure in property development and how service conditions affect polymeric product performance.
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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".