Crystallization Behavior of Poly[(butylene succinate)‐<i>co</i>‐adipate] Nanocomposite
Bibliographic record
Abstract
Abstract Summary: The unique crystallization behavior of a poly[(butylene succinate)‐co‐adipate] (PBSA) nanocomposite is addressed. Nanocomposites have been prepared by melt blending PBSA and organically modified synthetic fluorine mica (OSFM) in a batch mixer. The structure of the nanocomposite is studied by using X‐ray diffraction and transmission electron microscopy, which reveal a coexistence of exfoliated and intercalated silicate layers homogeneously dispersed in the PBSA matrix. The non‐isothermal crystallization behavior of PBSA and the nanocomposite samples is studied by differential scanning calorimetry (DSC). Various models, namely the Avrami method, the Ozawa method, and the combined Avrami‐Ozawa method, are applied to describe the kinetics of the non‐isothermal crystallization. All analyses reveal that the incorporation of the OSFM alters the crystallization properties of PBSA but in ways unexpected from other polymer nanocomposite systems. Polarized optical microscopy is used to support this conclusion. The activation energy for the non‐isothermal crystallization of both samples is evaluated by using three different methods. The results show that the absolute value of the activation energy for the nanocomposite is higher than that of the neat polymer. This indicates the slower crystallization kinetics of the nanocomposite. The effect of incorporation of OSFM on the cold crystallization behavior of neat PBSA is also studied by both conventional and temperature‐modulated DSC. Polarized optical microscopy image of the PBSA/OSFM nanocomposite at 70 °C during non‐isothermal crystallization from the melt. magnified image Polarized optical microscopy image of the PBSA/OSFM nanocomposite at 70 °C during non‐isothermal crystallization from the melt.
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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".