Cotton-quality fibers from complexation between anionic and cationic cellulose nanoparticles
Bibliographic record
Abstract
Natural polymers are attractive sustainable materials for production of fibers and composite materials. Cotton and flux are traditional plants used to produce textiles with comforting properties while technologies like Viscose, Lyocell and Ioncell-F allowed to extent fiber use into regenerated cellulose from wood. Neither natural nor man-made fibers completely satisfy the needs for cellulose based fabrics boosting development of new approaches to bring more sustainability into the fashion. Technologies like Spinnova are arising based on the spinning of mechanically pretreated cellulose materials with a lower environmental impact though challenged by the fiber quality and strength related to the inconsistency of the mechanical fibers. Nanoscaled cellulose is an excellent solution to improve the consistency of spin fibers, but charges introduced by traditional chemical treatments prevent rebuilding native hydrogen bonding and compromise the mechanical properties especially in wet conditions. We used nanocellulose with low surface charge isolated using reactive eutectic media to spin fibers able to restore the native hydrogen bonding and enable constitutional mechanical strength of cellulose. We performed un-optimized spinning to reveal the intrinsic properties of the fibers and confirmed the preserved strength of wet fibers compliant with the low surface charge enabling further engineering towards cotton-like fabric from wood.
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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) | 0.002 | 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".