Cellulose Nanocrystals Derived from Textile Waste through Acid Hydrolysis and Oxidation as Reinforcing Agent of Soy Protein Film
Bibliographic record
Abstract
More than 10 million tons of textile waste are disposed through landfill every year in North America. The disposal of textile waste via landfill or incineration causes environmental problems and represents a waste of useful resources. In this work, we explored the possibility to directly extract cellulose nanocrystals (CNCs) from untreated textile waste through two methods, namely sulfuric acid hydrolysis and three-step oxidization. CNCs with cellulose Iβ crystalline structure and rod-like shape were successfully obtained. The aspect ratios of CNCs prepared from acid hydrolysis and oxidization were 10.00 ± 3.39 and 17.10 ± 12.85, respectively. Their application as reinforcing agent of soybean protein isolate (SPI) film was evaluated. With the addition of 20% CNCs, the composite film maintained the high transparency, while their water vapor barrier property, tensile strength, and Young’s modulus were significantly improved. This research demonstrates a promising approach to recycle textile waste, and more value-added applications based on the derived CNCs could be expected.
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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) | 0.000 | 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 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".