Applying macromolecular crowding models to simulations of cellulose nanocrystals assembly

Self-assembly is a common phenomenon used by nature to organize matter to create multifunctional structures from molecular to macroscale. Cellulose nanocrystals (CNCs), are charged anisotropic particles that can spontaneously assemble into left-handed chiral nematic structures. Generally, the assembly of colloidal particles is a function of concentration, aspect ratio (AR), and polydispersity. While it is relatively easy to understand the main drivers of self-assembly through modeling these parameters, the left-handed chirality and the origin of twisting the CNC-based self-assembled systems are still debatable. To address this, we applied the crowding factor (CF), a parameter defined by the combination of AR and concentration, to facilitate the chiral twist in CNCs’ self-assembly using a molecular dynamic coarse-grained method with Gay-Berne potential.

Through our modeling work, we investigate the implication of macromolecular crowding in a periodic system that contains symmetric ellipsoidal particles. Our current results indicate two key stages in ellipsoidal particle assembly; in the first stage, dissociated particles assemble into small pre-clusters. This is followed by, the aggregation of the pre-clusters into larger bundles that indicate the spontaneous formation of a unidirectional twist. Our statistical analysis confirms that the CF theory can be extended to low-aspect ratio particle systems. Furthermore, our modeling demonstrates that systems with the same CF are likely to yield similar structures. Further studies on high CF media show that initial small clusters form at an early stage and induce a twist in these structures that non-selectively form a chiral structure, due to lack of center of chirality in pure ellipsoidal geometry.