Low-energy and simple synthesis of individualized cationic pH-responsive tertiary-aminated cellulose nanofibers and chitin nanocrystals by mechanochemistry and aging
Bibliographic record
Abstract
Cellulose and chitin nanomaterials are promising sustainable materials that exhibit attractive mechanical, optical, thermal, and chemical properties. Cellulose nanofibers (CNFs) have found applications to the field of packaging, reinforced composite or biomedical applications. Introducing charged functional groups onto these nanomaterials is a proven strategy to improve their dispersibility and processability, as well as their properties, such as adsorption capacity. The use of high energy defibrillators has remained necessary to access CNFs despite the introduction of surface charges prior to increase the efficiency of nanomaterial extraction. To date, there is no known synthesis of cationic CNFs (CCNFs) that is both energy efficient in the defibrillation, and chemically efficient in material modification. Herein we report a strategy to access CCNFs directly from once-dried wood pulp through mechanochemical and aging-based nucleophilic substitution, followed by a short sonication. This treatment introduces pH-responsive cationic diethylethylamine (DEEA) groups with a degree of substitution (DS) as high as 0.80 (amine content of 3.29 mmol g-1) without the use of excess reagents. The combination of short mechanochemical treatment (10 min), with aging and sonication allows access to high quality, 2-nm-wide, 1-μm-long CCNFs with high crystallinity of 56.6% and high ζ-potential of 68.10 ± 1.43 mV from sheets of pulp. The method was also applied to powder microcrystalline cellulose and chitin, to afford cationic nanocrystals of cellulose and chitin.
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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".