Colloidal Stability Window for Carboxylated Cellulose Nanocrystals: Considerations for Handling, Characterization, and Formulation
Bibliographic record
Abstract
The scale of production of cellulose nanocrystals (CNCs) has increased dramatically to meet the growing demand for sustainably sourced materials. This work defines the colloidal stability window for commercially produced carboxylated CNCs (DextraCel) compared to the more traditional sulfated CNCs. Phase diagrams showing the stable, reversibly agglomerated, irreversibly aggregated/sedimented, and colloidal glass “zones” as a function of suspension pH, ionic strength, CNC surface charge content, counterion, and concentration are presented. The p K a of carboxylated CNCs was measured to be 5.1, and suspensions of carboxylated CNCs (0.5–1.5 wt %) were visually stable from pH 3 to 11 (without salt). Carboxylated CNCs were highly sensitive to ionic strength, demonstrating some agglomeration with as little as 5 mM NaCl, supporting that weak acid surface groups and lower charge contents make CNCs more sensitive to solution conditions. Surface charge content had the greatest influence on colloidal stability followed by the counterion; carboxylated CNCs were more stable in the “as-received” sodium form, whereas sulfated CNCs had improved stability in acid form after ion exchange. The stability of carboxylated CNCs with industrially relevant additives (ionic and nonionic surfactants and initiators) was also investigated. Less concentrated suspensions were more colloidally stable, emphasizing that characterization and processing of CNCs favor dilute conditions. If carboxylated CNCs are subjected to conditions outside of their colloidal stability window, simple dilution or pH adjustment does not return them to colloidally stable discrete nanoparticles; however, ultrasonication can redisperse agglomerates. This study offers guidelines for handling carboxylated CNCs to broaden the range of products that can be improved by their incorporation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".