Rheological insights on the evolution of sonicated cellulose nanocrystal dispersions
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are promising biomaterials, but their tendency to agglomerate when dried limits their use in several applications. Ultrasonication is commonly used to disperse CNCs in water, bringing enough energy to the suspension to break agglomerates. While the optimized parameters for sonication are now well defined for small volumes of low concentration CNC suspensions, a deeper understanding of the influence of the dispersing process is needed to work with larger volumes, at higher concentrations. Herein, rheology is used to define the distribution and dispersion states upon ultrasonication of a 3.2 wt% CNC suspension. After considering the importance of the measurement sampling volume, the behavior of a more concentrated suspension (6.4 wt%) is examined and compared with a never-dried suspension of the same concentration to validate the dispersion state.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".