Polymer Grafted Nanocomposites (GNP) have improved mechanical, optical, thermal, and electrical properties over their unfilled polymer counterparts 1. The filler size, distribution, and dispersion can change each property significantly2. It is found that materials with well-dispersed fillers have enhanced properties. One of the outstanding challenges in NP-based colloids is aggregation, which can be controlled by grafting polymer chains on the NP surface. Polymer brushes have several applications, including colloidal stabilization and lubricants. Transport properties like viscosity, thermal conductivity, etc. heavily depend on parameters like NP or filler loading, polymer grafting density, polymer chain length, and so on3. While it is well-appreciated that nanocomposite flow behavior is critically affected by filler dispersion, the roles of the matrix and the polymeric grafts remain poorly understood. Due to their improved solubility and stability, GNPs are usually superior to bare nanoparticles when acting as surfactants in enhanced oil recovery processes. In many of these situations, nanoparticles are transported through a complex fluid to reach an intended target or to achieve a desired dispersity4. Although experimental methods have provided significant insight into the behaviour of nanoparticles and polymers in solutions and melts, existing techniques cannot probe physical mechanisms that dictate short-time and length-scale coupling between dynamics of particles and polymers in solution due to their limited spatiotemporal resolution. A mesoscale simulation method is a powerful modelling technique that enables the structure and dynamics of soft matter systems to be probed simultaneously. We plan to address the composition-structure-property relations of GNPs which are very poorly understood. Specifically, evaluating the viscosity of GNPs system as functions of crucial parameters like NP loading, grafting density, polymer chain-length, matrix chain-length and so on, relating the structural property to the transport property of the system. We also planned to look at the recent experimental observation in the feed processability conditions (Feed Pressure) in preparing GNPs affects the permeability of these systems. Finally, we have tried to quantify the effective potential of mean force (PMF) between a pair of GNPs using Machine Learning (ML) based algorithm. The grafting induces anisotropic self-assembly, it becomes difficult to quantify the PMF. By training the model one can predict the self-assembly of these GNPs.
References
(1) Dean, J.; Taylor, M. G.; Mpourmpakis, G. Unfolding Adsorption on Metal Nanoparticles: Connecting Stability with Catalysis. Sci. Adv. 2019, 5 (9), eaax5101. https://doi.org/10.1126/sciadv.aax5101.
(2) Jiao, Y.; Akcora, P. Understanding the Role of Grafted Polystyrene Chain Conformation in Assembly of Magnetic Nanoparticles. Phys. Rev. E 2014, 90 (4), 042601. https://doi.org/10.1103/PhysRevE.90.042601.
(3) Hattemer, G. D.; Arya, G. Viscoelastic Properties of Polymer-Grafted Nanoparticle Composites from Molecular Dynamics Simulations. Macromolecules 2015, 48 (4), 1240–1255. https://doi.org/10.1021/ma502086c.
(4) Liu, J.; Gao, Y.; Cao, D.; Zhang, L.; Guo, Z. Nanoparticle Dispersion and Aggregation in Polymer Nanocomposites: Insights from Molecular Dynamics Simulation. Langmuir 2011, 27 (12), 7926–7933. https://doi.org/10.1021/la201073m.