Mechanical properties of thermally treated hemp fibers in inert atmosphere for potential composite reinforcement
Bibliographic record
Abstract
Hemp (Cannabis Sativ L.) is an important lignocellulosic raw material for the manufacture of cost-effective environmentally friendly composite materials. From an earlier study it was found that when hemp bast fibers were heated above the glass transition temperature of lignin, there was a migration of lignin to the surface of the fiber. The preliminary observations showed that heat treatment in inert environment seemed to provide enough fiber opening without affecting the associated tissues of the fibers. Here, hemp fibers were given heat treatment in an enclosed vessel in air as well as inert environment and their mechanical properties were compared to the raw hemp fiber. It was found that there were openings of fibers upon heating, both along the length as well as along the diameter or the width directions. For the same weight of the fiber, the total count of fibers increased during heat treatment, with increment up to 32% for inert environment and 39% for air environment; the increment was mainly due to opening up of fibers into lesser diameters than the original fibers. The strength properties were strongly influenced by the diameter of the fibers, with the lesser fibers contributing to greater tensile strength and modulus. The overall tensile strength and modulus of fibers treated in inert environment were found to have increased, probably due to production of fibers of lesser diameters, presumably with less number of natural defects. The overall strength of fiber treated in air environment, however, decreased even though there was opening up of fibers in this case as well. This was due to oxidation of various constituents of fiber which contributes strength.
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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".