Effect of evolved interactions in poly(butylene succinate)/fumed silica biodegradable <i>in situ</i> prepared nanocomposites on molecular weight, material properties, and biodegradability
Bibliographic record
Abstract
Abstract Poly(butylene succinate) (PBSu)/fumed silica nanocomposites were prepared in situ by condensation polymerization. TEM micrographs verified that the dispersion of the nanoparticles was homogeneous in the PBSu matrix, while some small agglomerates were also formed at a higher SiO2 content. 13C NMR spectra affirmed that the hydroxyl end groups of PBSu could form covalent bonds with the surface silanol groups of SiO2. These interactions affected the molecular weight of the prepared nanocomposites. At low concentrations the SiO2 nanoparticles acted as chain extenders, increasing the molecular weight of PBSu, while at higher loadings they resulted in extended branching and crosslinking reactions, leading to gradually decreased molecular weights. Silica nanoparticles acted as nucleating agents, increasing the crystallization rate of PBSu. However, the degree of crystallinity was slightly reduced. Tensile strength and Young's modulus were significantly increased with increasing SiO2 content. The presence of the nanoparticles resulted in reduced enzymatic hydrolysis rates compared to pure PBSu, attributed to the smaller available organic surface, due to the incorporation of SiO2, and to the existence of branched and crosslinked macromolecules. Dynamic mechanical and rheological properties were also extensively studied. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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) | 0.001 | 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".