Producing light-weight bast fibers from canola biomass for technical textiles
Bibliographic record
Abstract
Due to the excessive use of water required for cotton cultivation, scientists in this field have been looking at waste biomass as an alternative source of fiber supply. Canola waste biomass is a source of textile fibers which effectively costs nothing, as the biomass can be collected from the waste plant stems of canola plants after harvesting. Therefore, an investigation has been conducted to identify the characteristics of canola fiber and of the canola cultivar ( Brassica napus L.) suitable for textile applications. In this research, a bio-inspired approach was applied to produce fiber from canola biomass by water retting of four different cultivars (HYHEAR 1, Topas, 5440, and 45H29) cultivated in a greenhouse under controlled atmospheric conditions. It was found that the structural hierarchy of fiber density, mechanical properties and other textile fiber properties of canola fiber differ from cultivar to cultivar, which can be carefully harnessed for different applications. Further, it was found that the density of canola fiber is much lower than that of cotton and other competitive bast fibers, owing to its hollow structure, as revealed by scanning electron microscopy. The results suggest that canola may be an excellent choice for manufacturing of non-woven fabrics, eco-composites, apparel or other technical textiles.
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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 | 0.001 | 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".