Functionalizing Cellulose Nanocrystals with Click Modifiable Carbohydrate-Binding Modules
Bibliographic record
Abstract
Functionalized cellulose nanocrystals (CNC) have unique properties that make them attractive in various applications such as drug delivery, hydrogels, and emulsions. However, the predominant chemical methods currently used to functionalize cellulose nanocrystals have a large environmental footprint. Although greener methods are desirable, the relatively inert nature of cellulose crystals presents a major challenge to their potential modification in aqueous media. In the work reported here, carbohydrate binding modules (CBMs) were used to introduce new functionality to cellulose surfaces. CBM2a, which has a strong affinity for crystalline cellulose, was functionalized with an alkyne at the terminal amine position. The alkyne group, which was introduced onto the cellulose surface with CBM2a, underwent a Click reaction with polyethylene glycol (PEG) to modify CNC surfaces. This provided a strong, non-covalent modification of cellulose surfaces that was carried out in a one-pot reaction in aqueous media. The CBM-PEG modification of cellulose surfaces increased CNC redispersion after drying and improved suspension stability based on steric interactions. It was apparent that hybrid polysaccharide-protein, self-assembled nanoparticles could be effectively produced, with potential for nanomedicine, immunoassay, and drug delivery applications.
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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".