Dispersing swimming microalgae in self-assembled nanocellulose suspension: Unveiling living colloid dynamics in cholesteric liquid crystals
Bibliographic record
Abstract
Active matter comprises individual energy-consuming components that convert locally stored energy into mechanical motion. Among these, liquid crystal dispersed self-propelled colloids have displayed fascinating dynamic effects and nonequilibrium behaviors. In this work, we introduce a new type of active soft matter based on swimming microalgae and lyotropic nanocellulose liquid crystal. Cellulose is a kind of biocompatible polysaccharide that nontoxic to living biological colloids. In contrast to microalgae locomotion in isotropic and low viscosity media, we demonstrate that the propulsion force of swimming microalgae can overcome the stabilizing elastic force in cholesteric nanocellulose liquid crystal, with the displacement dynamics (gait, direction, frequency, and speed) be altered by the surrounding medium. Simultaneously, the active stress and shear flow exerted by swimming microalgae can introduce local perturbation in surrounding liquid crystal orientation order. The latter effect yields hydrodynamic fluctuations in bulk phase as well as layer undulations, helicoidal axis splay deformation and director bending in the cholesteric assembly, which finally followed by a recovery according to the inherent viscoelasticity of liquid crystal matrix. Our results point to an unorthodox design concept to generate a new type of hybrid soft matter that combines nontoxic cholesteric liquid crystal and active particles, which are expected to open opportunities in biosensing and biomechanical applications.
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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 | 0.000 |
| 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 | 0.000 |
| 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".