The Physicochemical Properties of Hemp Fibers and Their Applications in the Textile Industry
Bibliographic record
Abstract
This study explores the physicochemical properties of hemp fibers and their potential applications in the textile industry, including the effects of various chemical and physical treatments on the quality and performance of hemp fibers. The research found that after chemical treatments with sodium hydroxide and potassium permanganate, the fineness, flexibility, and tensile strength of hemp fibers were significantly improved. The combination of microwave energy and deep eutectic solvent treatments effectively removed non-cellulosic substances, increased cellulose content, and improved thermal stability. Processing hemp fibers using industrial flax equipment demonstrated high processing efficiency, with fiber quality comparable to flax. Economic analysis suggests that hemp fibers could become a more sustainable and cost-effective alternative to cotton in the textile industry. The results indicate that with appropriate chemical and physical treatments, hemp fibers can meet the quality standards required for high-performance textile applications. This study highlights the potential of hemp as a sustainable and economically viable alternative to traditional textile fibers, such as cotton and flax, promoting the use of green biomaterials in the textile industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".