Investigation of the Influence of Different Filler Contents of Wine Pomace in PBS on Fracture Mechanics Properties
Bibliographic record
Abstract
Biobased polybutylene succinate (PBS) represents a promising alternative to petrochemical-based polymers. The use of this biopolymeris limited in many areas by its low resilience against environmental influences. With the help of bio-based stabilizers the thermo-oxidative degradation process can be slowed down. Suitable stabilizing additives are natural antioxidants present in plant extracts with a high flavonoid content, which can be found in grapes, wine and wine by-products. PBS was modified with two different bio-stabilizers based on wine grape pomace. The highest filler content tested was 20 wt.-%. In addition to improving stability, the additives also impact the polymer’s mechanics. The influence of these functional fillers on the fracture mechanical properties was examined in a quasi-static test. The crack growth was recorded using integrated video monitoring. Based on the results, the corresponding crack resistance curve and tearing modulus were determined depending on filler type and content. Additional optical analysis was used to correlate fracture mechanics and structure. The two bio-stabilizers based on red (RWP) and white wine pomace (WWP) differs distinctly in terms of their influence on fracture mechanical properties. The Influence of RWPon the fracture toughness is significantly higher than that of WWP. Especially at lower filler contents with RWP, there is a strong increase in the fracture mechanics parameter tearing modulus (TJ) and an increase in the slope of the R-curve. With 5 wt.% RWP DOM the TJ is 13.64 x 102, whereas with WWP Silv a value of only 6.39 x 102 can be achieved. This difference is also reflected in the increase in the R-curves. With 5 wt.% a slope of the fitted R-curve of 265.59 (RWP DOM) and 121.02 (WWP Silv) could be determined with the first derivative. In the optical analysis it was noticeable that the RWP particles were more homogeneously dispersed in the matrix while the WWP filler tended to agglomerate. The inhomogeneous distribution and strong agglomeration tendency can be attributed to a higher sugar content of WWP and a higher particle size distribution. The top cut (D97) of WWP Silv is 62.37 ± 0.05 µm and that of RWP DOM is 51.97 ± 0.09 µm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".