Dynamic and Static Assembly of Sulfated Cellulose Nanocrystals with Alkali Metal Counter Cations
Bibliographic record
Abstract
Sulfate groups on cellulose particles such as cellulose nanocrystals (CNCs) provide colloidal stability credit to electrostatic repulsion between the like-charged particles. The introduction of sodium counter cations on the sulfate groups enables drying of the CNC suspensions without irreversible aggregation. Less is known about the effect of other counter cations than sodium on extending the properties of the CNC particles. Here, we introduce the alkali metal counter cations, Li+, Na+, K+, Rb+, and Cs+, on sulfated CNCs without an ion exchange resin, which, so far, has been a common practice. We demonstrate that the facile ion exchange is an efficient method to exchange to any alkali metal cation of sulfate half esters, with exchange rates between 76 and 89%. The ability to form liquid crystalline order in rest was observed by the presence of birefringence patterns and followed the Hofmeister series prediction of a decreasing ability to form anisotropy with an increasing element number. However, we observed the K-CNC rheology and birefringence as a stand-out case within the series of alkali metal modifications, with dynamic moduli and loss tangent indicating a network disruptive effect compared to the other counter cations, whereas observation of the development of birefringence patterns in flow showed the absence of self- or dynamically-assembled liquid crystalline order.
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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".