The Effect of Zn <sub>2</sub> (BDC) <sub>2</sub> Metal Organic Framework on the Miscibility and Properties of Poly(Lactic Acid)/Poly(Butylene Adipate-co-Terephthalate) Biodegradable Blends
Bibliographic record
Abstract
In our research described in this paper, the metal-organic framework MOF-2 [Zn2(BDC)2] was used to enhance the properties of a biodegradable blend based on poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT). PLA/PBAT/(1, 3, 5, and 7 wt%) MOF-2 blends were prepared using a melt blending technology with a twin-screw extruder. The phase morphology, rheological behavior and barrier properties of the blends were studied. X-ray diffraction (XRD) showed a slight increase in the crystalline content of the PLA/PBAT matrix with increasing MOF-2 content. Scanning electron microscopy (SEM) revealed that the PLA/PBAT blend was immiscible, and the phase morphology improved with MOF-2 by enhancing the interfacial compatibility, reducing the size of micro-domains and cavities, and promoting a transition to a co-continuous morphology, indicating better miscibility. The ternary nanocomposites with 5 and 7 wt% MOF-2 showed increased complex viscosity (η*) and storage modulus (G′) compared to those with 1 and 3% MOF-2, indicating stronger percolated networks. The thermal and barrier properties and water absorption capacity were also improved, confirming the rheological measurements. All the results showed that the PLA/PBAT/MOF-2 nanocomposites could be used in the field of 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".