Textiles from non-wood feedstocks: Challenges and opportunities of current and emerging fiber spinning technologies
Bibliographic record
Abstract
As the global population continues growing, the demand for textiles also increases, putting pressure on cotton manufacturers to produce more natural fiber from this already undersupplied resource. Synthetic fibers such as polyester (PET) can be manufactured quickly and cheaply, but these petroleum-based products are detrimental to the environment. With increased efforts to encourage transparency and create a more circular textile economy, other natural alternatives must be considered. This article discusses the existing condition and future possibilities for man-made cellulosic fibers (MMCFs), with an emphasis on using non-woody alternative feedstocks as a starting material. This work focuses on conversion technology suitable for producing textile-grade fibers from non-wood-based dissolving pulp, which may be different in nature from its woody counterpart and therefore behave differently in spinning processes. Derivatization and dissolution methods are detailed, along with spinning techniques and parameters for these processes. Existing research related to the spinning of non-woody-based dissolving pulp is covered, along with suggestions for the most promising feedstock and technology combinations. In addition, an emerging method of conversion, in which textile fibers are spun from a hydrogel made of an undissolved nano/micro-fibrillated fiber suspension, is briefly discussed due to its unique potential. Methods and concepts compiled in this review relate to utilizing alternative feedstocks for future fibers while providing a better understanding of conventional and emerging fiber spinning processes for these fibers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".