Nanopaint has drawn great attention in recent years, as it may offer superior mechanical and/chemical properties with the resulting coating (e.g., scratch resistance). The known experimental efforts in this area have improved our understanding on coating microstructure and its correlation with coating properties. However, due to the design and experimental complexity as well as cost concern, how to optimally design nanopaint in a cost effective way is extremely challenging.

In this paper, we will introduce a computational nanopaint design methodology by resorting to off-lattice Monte Carlo modeling and simulation techniques. This methodology contains a unified coarse-grained model that is for characterizing comprehensive interactions among monomers, crosslinkers and spherical nanoparticles. By off-lattice MC, coating microstructures can be generated and computational tensile tests can be then conducted to evaluate the stress-strain behavior of the coating, which is critical to revealing the coating tensile property and scratch resistance performance. In this way, various comprehensive and quantitative correlations among nanopaint material, coating microstructure, coating property, and coating quality can be established. In order to have an in-depth understanding on the structure-property correlations, the microstructures are further characterized by quantifying the spatial distribution of polymer beads surrounding nanoparticles. The introduced characterization method has unique capability for analyzing the nanopaint material, where the distribution of the monomers and the crosslinkers can be differentiated for further investigation. Furthermore, a new stress evaluation method is presented, which can enable a thorough investigation on different stress contributors (i.e., polymer-polymer stress, polymer-nanoparticle stress, nanoparticle-nanoparticle stress) and their evolutions during a tensile test. Such a stress partitioning study is crucial for gaining a better understanding of the deformation behavior of the nanopaint material.

An acrylic-melamine-alumina nanoparticle contained nanopaint design is selected as a case study to demonstrate the attractiveness of the introduced methodology. In this study, the effect of polymer-nanoparticle interaction strength and that of nanoparticle size on coating tensile property and scratch resistance performance are thoroughly investigated. It is found that adding nanoparticles into a convectional paint sample may not be guaranteed for coating performance improvement. A key for achieving the enhancement is to ensure that the polymer-nanoparticle interaction strength is higher than a critical value. It is also identified that a nanopaint material containing smaller nanoparticles can have a better scratch resistance performance, but it requires more energy for curing in coating manufacturing. Thus, nanopaint designers should properly make trade-offs between product performance and process efficiency.