Effect of Ionic Surfactants on the Viscoelastic Properties of Chiral Nematic Cellulose Nanocrystal Suspensions
Bibliographic record
Abstract
The gelation of cellulose nanocrystal (CNC) suspensions is explored in the presence of two ionic surfactants, namely, sodium dodecyl sulfate (SDS) with a negatively charged head and cetyltrimethylammonium bromide (CTAB) with a positively charged head. The viscosity profile of pure CNC suspensions indicated that at concentrations greater than 5 wt %, they exhibit gel-like behavior, where the viscosity shows a single shear-thinning profile. However, at concentrations lower than 1 wt %, the suspension shows isotropic behavior and experiences a transition to chiral nematic biphasic domains at increasing CNC concentration. In addition, the effect of CTAB, SDS, and NaCl on the surface charge of CNCs is analyzed and coupled with rheological measurements in order to study the relative importance of surfactants and ionic strength on the viscoelastic properties of the CNC suspensions. The mechanism of CNC/surfactants and CNC/CNC interactions leading to the gelation of CNC suspensions is also investigated. It was found that the addition of both ionic surfactants results in the gelation of biphasic chiral nematic CNC suspensions by fostering cross-linking between individual CNCs at smaller concentrations. In the case of CTAB, a gradual and ongoing increase in the viscoelastic moduli was observed with the increase of surfactant concentration, implying the induction of dominant attractive forces between CNCs by surfactant molecules. However, the presence of SDS stimulates both attractive and repulsive forces whose relative dominance controls the viscoelastic properties and gelation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".