Anisotropic foams derived from textile-based cellulose nanocrystals and xanthan gum
Bibliographic record
Abstract
The upcycling of discarded garments can help to mitigate the environmental impact of the textile industry. Here, we fabricated hybrid anisotropic foams having cellulose nanocrystals (CNCs), which were isolated from discarded cotton textiles and had varied surface chemistries as structural components, in combination with xanthan gum (XG) as a physical crosslinker of the dispersion used for foam preparation. All CNCs had crystallinity indices above 85 %, zeta potential values below −40 mV at 1 mM NaCl, and true densities ranging from 1.61 to 1.67 g·cm−3. Quartz crystal microbalance with dissipation (QCM-D) measurements indicated weak interactions between CNC and XG, while rheology measurements showed that highly charged CNCs caused the XG chains to change from an extended to a helicoidal conformation, resulting in changes the in viscoelastic properties of the dispersions. The inclusion of XG significantly enhanced the compression mechanical properties of the freeze-casted foams without compromising their thermal properties, anisotropy, or degree of alignment. CNC-XG foams maintained structural integrity even after exposure to high humidity (91 %) and temperatures (100 °C) and displayed very low radial thermal conductivities. This research provides a viable avenue for upcycling cotton-based clothing waste into high-performance 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) | 0.000 | 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".