Strengthening Cellulose Nanopaper via Deep Eutectic Solvent and Ultrasound-Induced Surface Disordering of Nanofibers
Bibliographic record
Abstract
The route for the preparation of cellulose nanofiber dispersions from bacterial cellulose using ethylene glycol- or glycerol-based deep eutectic solvents (DES) is demonstrated. Choline chloride was used as a hydrogen bond acceptor and the effect of the combined influence of DES treatment and ultrasound on the thermal and mechanical properties of bacterial cellulose nanofibers (BC-NFs) is demonstrated. It was found that the maximal Young's modulus (9.2 GPa) is achieved for samples prepared using a combination of ethylene glycol-based DES and ultrasound treatment. Samples prepared with glycerol-based DES combined with ultrasound exhibit the maximal strength (132 MPa). Results on the mechanical properties are discussed based on the structural investigations that were performed using FTIR, Raman, WAXD, SEM and AFM measurements, as well as the determination of the degree of polymerization and the density of BC-NF packing during drying with the formation of paper. We propose that the disordering of the BC-NF surface structure along with the preservation of high crystallinity bulk are the key factors leading to the improved mechanical and thermal characteristics of prepared BC-NF-based papers.
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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".