Isolation of Nano Fibres from Hemp and Flax and Their Thermoplastic Composites——Hemp and Flax Nanofibres and Composites
Bibliographic record
Abstract
Agricultural feed stocks are one of the readily available and inexpensive lignocellulosic feed stocks for preparing cellulose nano fibrils. The very common method for isolation and separation of cellulosic fibrils from these feed stocks include successive physicochemical techniques. In this paper, a mild chemical treatment was used to remove pectinic substances and lignin, following a mechanical defibrillation using a commercial grinder to isolate nano fibres from agricultural fibres such as flax and hemp. The defibrillation process was optimized based on the strength properties of the nanofibril films. The generated nano fibres have a diameter distribution in the range of 20-100 nm with 80% fibrils in the range of 20-60 nm. The tensile strength and the modulus of the nano papers were 180-200 MPa and 9-11 GPa compared to 100-95 MPa and 5-6 GPa respectively for hemp and flax. The study showed that, the nano papers can impart high strength even in the presence of hemicelluloses present ed in the fibre bundle indicating removal of hemicelluloses to very low extent using intensive chemical treatments which may not be required for feed stocks with fewer amounts of hemicelluloses, such as agricultural fibres. To exploit the strength properties and reinforcing potential of these nano papers, composites were prepared with polycarbonate. The prepared modulus of the composites showed a significant improvement at a fibre loading of 13 wt%.
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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) | 0.001 | 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".