Controlling the Nematic Liquid Crystallinity of Cellulose Nanocrystals with an Alcohol Ethoxy Sulfonate Surfactant
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are biobased colloidal nanorods that have unlocked new opportunities in the area of sustainable functional nanomaterials including structural films and coatings, biomedical devices, energy, sensing, and composite materials. While selective light reflection and sensing develop from the typical chiral nematic (cholesteric, Nem*) liquid crystallinity exhibited by CNCs, a wealth of technologies would benefit from a nematic liquid crystal (LC) with CNC uniaxial alignment. Therefore, this study answers the central question of whether surfactant complexation suppresses CNC chirality in favor of nematic lyotropic and thermotropic liquid crystallinity. Therein, we use a common surfactant having both nonionic and anionic blocks, namely, oligo(ethylene glycol) alkyl-3-sulfopropyl diether potassium salt (an alcohol ethoxy sulfonate (AES)). AES forms complexes with CNCs in toluene (a representative for nonpolar organic solvent) via hydrogen bonding with an AES' oligo(ethylene glycol) block. A sufficiently high AES weight fraction endows the dispersibility of CNC in toluene. Lyotropic liquid crystallinity with Schlieren textures containing two- and four-point brush defects is observed in polarized optical microscopy (POM), along with the suppression of the cholesteric fingerprint textures. The results suggest a nematic (Nem) phase in toluene. Moreover, thermotropic liquid crystallinity is observed by incorporating an excess of AES, in the absence of an additional solvent and upon mild heating. The Schlieren textures suggest a nematic system that undergoes uniaxial alignment under mild shear. Importantly, replacing AES with a corresponding nonionic surfactant does not lead to liquid crystalline properties, suggesting electrostatic structural control of the charged end group of AES. Overall, we introduce a new avenue to suppress CNC chirality to achieve nematic structures, which resolves the long-sought uniaxial alignment of CNCs in filaments, composite materials, and optical devices.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".