Regioselective and Water-Promoted Surface Esterification of Never-Dried Cellulose Fibers Towards Nanofibers with Adjustable Surface Energy
Bibliographic record
Abstract
A new regioselective route is introduced for surface modification of biological colloids in the presence of water. Taking the case of cellulose nanofibers (CNFs), we demonstrate a site-specific (93% selective) reaction between the primary surface hydroxyl groups (C6-OH) of cellulose and acyl imidazoles. CNFs bearing C6-acetyl and C6- iso -butyl groups, with a degree of substitution up to 1 mmol·g –1 were yielded upon surface esterification, affording nanofibers of adjustable surface energy. The morphological and structural features of the nanofibers remained largely unaffected, but the regioselective surface reactions enabled tailoring of interfacial interactions, as demonstrated in oil/water Pickering emulsions. Our method precludes the need for drying or solvent exchange to organic solvents for surface esterifications, which are otherwise demanded in the fabrication of esterified colloids and polysaccharides and can be potentially implemented into reactive extrusion and compounding of high-solid content colloids. The proposed acylation is expected to open new and sustainable approaches that benefit from the presence of water and the high selectivity.
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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".