Mesoscale Modeling of Agglomeration of Molecular Bottlebrushes: Focus on Conformations and Clustering Criteria

Abstract

Using dissipative particle dynamics, we characterize dynamics of aggregation of molecular bottlebrushes in solvents of various qualities by tracking the number of clusters, the size of the largest cluster, and an average aggregation number. We focus on a low volume fraction of bottlebrushes in a range of solvents and probe three different cutoff criteria to identify bottlebrushes belonging to the same cluster. We demonstrate that the cutoff criteria which depend on both the coordination number and the length of the side chain allows one to correlate the agglomeration status with the structural characteristics of bottlebrushes in solvents of various qualities. We characterize conformational changes of the bottlebrush within the agglomerates with respect to those of an isolated bottlebrush in the same solvents. The characterization of bottlebrush conformations within the agglomerates is an important step in understanding the relationship between the bottlebrush architecture and material properties. An analysis of three distinct cutoff criteria to identify bottlebrushes belonging to the same cluster introduces a framework to identify both short-lived transient and long-lived agglomerates; the same approach could be further extended to characterize agglomerates of various macromolecules with complex architectures beyond the specific bottlebrush architecture considered herein.

Nanogel Degradation at Soft Interfaces and in Bulk: Tracking Shape Changes and Interfacial Spreading

“…The average is taken at time interval t ∈[35000:50000] using 150 frames). Large fluctuations of the shape anisotropy were previously reported while characterizing conformations of polymers of various architectures. ,− …”

“…κ 2 is typically used to characterize shapes of various polymeric species − and ranges from κ 2 = 0 for an ideal sphere to κ 2 = 0.25 for a planar object (with λ 1 = λ 2 and λ 3 = 0) to κ 2 = 1 for points on a line . For linear polymer chains, κ 2 ≈ 0.43 and ≈0.39 in good and theta solvents, respectively. − …”

Self-Assembly of Molecular Brushes with Responsive Alternating Copolymer Side Chains

“…Dissipative particle dynamics (DPD) simulations , were used to study the behavior of comblike polymers. − ,, Within the standard DPD framework, all polymer segments and solvent molecules are represented in terms of spherical beads of equal mass m , whereas each bead usually comprises a group of atoms. The beads interact with each other by a pairwise additive force boldF i = ∑ i ≠ j false( boldF i j normalC + boldF i j normalD + boldF i j normalR + boldF i j normalB false) where F ij C is a conservative force responsible for the repulsion via soft potential characterized by the parameter a ij : the bigger the value of a ij , the stronger the repulsion between the i th and j th beads.…”

“…Dissipative particle dynamics (DPD) simulations 32,33 were used to study the behavior of comblike polymers. [26][27][28]31,34 Within the standard DPD framework, all polymer segments and solvent molecules are represented in terms of spherical beads of equal mass m, whereas each bead usually comprises a group of atoms. The beads interact with each other by a pairwise additive force…”

Mesoscale modeling of random chain scission in polyethylene melts

“…The beads in DPD represent groups of atoms; the motion of DPD beads obeys Newton's laws of motion with the pairwise additive force encompassing purely repulsive conservative, dissipative, and random contributions acting between all the beads [20]; an additional bonding potential is introduced between the bonded beads. This highly computationally efficient approach has become an attractive computational tool to model a broad range of multicomponent systems [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Recent developments and applications of DPD can be found in recent reviews by Español and Warren [37] and Santo and Neimark [38].…”