Tuning Cellulose Nanocrystal Gelation with Polysaccharides and Surfactants
Bibliographic record
Abstract
Gelation of cellulose nanocrystal (CNC) dispersions was measured as a function of the presence of four nonionic polysaccharides. Addition of hydroxyethyl cellulose (HEC), hydroxypropyl guar (HPG), or locust bean gum (LBG) to CNC dispersions induced the gelation of dilute CNC dispersions, whereas dextran (DEX) did not. These behaviors correlated with adsorption tendencies; HEC, HPG, and LBG adsorbed onto CNC-coated quartz crystal microbalance sensors, whereas DEX did not adsorb. We propose that the adsorbing polysaccharides greatly increased the effective volume fraction of dilute CNC dispersions, driving more of the nanocrystals into anisotropic domains. SDS and Triton X-100 addition disrupted HEC-CNC gels whereas CTAB did not. Surface plasmon resonance measurements with CNC-coated sensors showed that SDS and Triton X-100 partially removed adsorbed HEC, whereas CTAB did not. These behaviors illustrate the complexities associated with including CNC dispersions in formulated products: low CNC contents can induce spectacular changes in rheology; however, surfactants and soluble polymers may promote gel formation or induce CNC coagulation.
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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 | 0.001 |
| 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 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".