Surface modification of Alberta based hemp fibers
Bibliographic record
Abstract
Chemical treatments are conducted in this study to modify lignocellulosic composition and to improve the surface characteristics and mechanical properties of the decorticated and retted Alberta (AB)-based hemp fibers, which are investigated for the first time to meet the standards required for the composite material applications. A detailed approach was adopted where physical and chemical treatments were performed to produce uniform and clean fibers. This was conducted to make the fibers suitable for processing and to determine factors such as lignocellulosic biomass (cellulose, hemicellulose, and lignin), tensile properties (strength and modulus), chemical treatment effects on fiber dimension (single fiber diameter), and the cost of single chemical cleaning, which can be together considered when choosing an optimal chemical treatment. Hydrogen peroxide (H 2 O 2 ) at concentrations of 4 %, 5 %, and 6 % vol/vol, and (3-Glycidyloxypropyl)trimethoxysilane (GPTMS) at 1 %, 5 %, and 20 % wt/wt treatments significantly altered the fiber composition and increased both cellulose and lignin content. GPTMS treatment at 1 % wt/wt, despite its effect on lignocellulosic content compared to H 2 O 2 , provided advantageous mechanical properties, balancing strength and consistent fiber performance with minimal variability. Notably, fibers treated with 1 % vol/vol GPTMS were diametrically smallest and showed the maximum increase of 65.08 % in tensile strength compared to untreated retted hemp fibers. From a cost standpoint, 1 % wt/wt GPTMS was the most economical at CAD $1.08 per gram of fiber, while the 6 % vol/vol H 2 O 2 treatment was significantly more expensive for manufacturing scalability. In conclusion, our findings highlight the potential of 1 % vol/vol GPTMS treatments to enhance the properties of untreated hemp fibers, and make them a viable and sustainable option for chemical treatment to produce high-performance sustainable materials.
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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".