Mechanical property enhancement of poly (butylene adipate-co-terephthalate)-based composites via filling with cellulose-rich biofiber from kimchi cabbage byproducts
Bibliographic record
Abstract
Despite their promise as industrial materials, kimchi cabbage byproducts (KCBs) are typically disposed of through landfilling or incineration. Previous research on KCB recycling has primarily focused on low-value applications, which do not fully exploit the potential of these byproducts as sources of cellulose-rich biofiber (CRB). To bridge this gap, this study investigated the ability of CRB extracted from KCBs to enhance the mechanical properties of poly (butylene adipate- co -terephthalate) (PBAT) composites. High-purity CRB with an improved whiteness index and thermal stability was effectively extracted using citric acid/H 2 O 2 pretreatment, an eco-friendly method outperforming conventional pretreatments based on strong acid- or halogen-containing chemicals in terms of toxicity and ease of operation. PBAT composite films with varying CRB contents (0–25 wt%) were fabricated using twin-screw compounding followed by compression molding. The mechanical and thermal property analyses of the PBAT/CRB composites revealed that the incorporation of 15 wt% CRB yielded an optimal balance between mechanical strength and flexibility, increasing Young's modulus and tensile strength without compromising elongation at break. The enhanced interfacial adhesion between CRB and PBAT revealed by morphological analysis was attributed to hydrogen bonding. These findings suggest that KCB-derived CRB is a promising biofiller for enhancing the mechanical properties of PBAT composites, offering a sustainable alternative for various industrial applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".