
Structure and dynamics of polymer networks using nonEuclidean geometry
AllocationAbstract
Polymer gels in the form of covalently crosslinked networks are important in, e.g., tissue engineering and for drug delivery, as well as in different types of actuators. These systems are also used for fundamental studies of transport processes in cells.
To investigate more general types of networks (e.g. microscopically anisotropic, coreshell particles), using networks with an unbiased distribution of crosslinking nodes, we have recently shown (ref. 1) how to construct a network on a hypersphere (S3) starting from a regular structure in R3. In this way, we will have a closed network where restrictions in terms of an underlying lattice structure or the connectivity across periodic boundaries can be avoided.
During last year, we developed a novel algorithm to create a closed network on S3 without any underlying bias from the starting structure in R3, randomly distributing the crosslinking nodes directly on the hypersphere. Looking at the diffusion of tracer particles in these gels showed the importance of the dynamics of the network on the diffusion process, and that particles sizes well above the maximum in the pore size distribution had a finite diffusion coefficient (ref. 2).
Another important feature of directly distributing the crosslinking nodes before forming a closed network, is that one can construct gel nanoparticles. We have recently used this to also look at the dynamics during collapse transitions for coreshell nanoparticles (ref. 3).
With these tools now available, we will continue to study the influence of inhomogeneities on different length scales, and how this will affect the diffusion of tracer particles. This is most relevant comparing with experimental data since it is know that crosslinked gel materials usually have a rather high degree of heterogeneity.
In parallel with this, we will also develop tools to analyse the structure of closed networks and gels living on e.g. a hypersphere.
The need for cputime would be in the range 3540000 cpu hours per month, and the disk space needed to store the resulting trajectories during the analysis stage would be at least 500Gb during the present part of the project. The resources are particularly needed, since we will have a new master student who will be part of this project from beginning of February.
1. N. Kamerlin et.al. J. Chem. Phys. 141 (2014) 154113
2. N. Kamerlin et. al., J. Phys., Cond. Matter 28 (2016) 475101
3. N. Kamerlin et.al., Macromolecules 49 (2016) 5740
