Conclusions

A comprehensive methodology and framework for the multiscale life-cycle-based sustainability assessment of nanocoating technology has been successfully developed. This novel technology was studied using computational modeling and experimental analysis on various levels of time and length ranging from nano- to macro- scales. These modeling methodologies and corresponding results generated key information required for the quantification of several sustainability matrixes representing different stages of the life cycle of nanocoating technology.

The key challenge to the research on sustainability assessment of an emerging technology such as “nanocoating technology” comes from the limited scientific knowledge and the reliable

data availability. Since last decade, nanoparticle-based coating materials are being studied extensively to determine all the attractive properties achievable with it. It has been proven that the addition of nanosize fillers of different types and shapes can enhance the polymer composite as well as coating resin‟s properties tremendously. The final product can have significantly improved mechanical stiffness, scratch resistance, super-corrosion resistance, antifouling properties, mar resistance and also certain smart functionalities such as self-cleaning, self- healing, thermochromic, heat resistance, significantly improved solvent and chemical resistance, etc. However, it is still unknown how these nanoparticles are responsible for such property enhancements. The experiments solely are not capable of answering questions and studying the microstructure-property correlations among nanoparticles and polymer resins. Through this research, an integrated computation-experimental approach was provided to study these microstructure-property-performance correlations.

The computational modeling was performed using molecular simulation techniques such as Monte-Carlo and Molecular Dynamics (MD). In the past, modeling methodology was developed on macro- to meso- scales of material. This work adopted the bottom-up, deductive systems engineering approach of materials development. The nanocoating system was simulated on nano-, micro- and meso- levels and the methodology was connected with the previously developed system to complete the multiscale modeling methodology of nanocoating system development.

The nanocoating material was first simulated through MD simulation technique on a microscale. The microstructural parameters such as bond lengths, bond angles, atomic charges, dihedrals, etc. were taken from a well-established atomistic force field called “CHARMM”. The atomistic system consisted of a large number of entities which made the simulation of a

nanocoating system with huge design space and complexity computationally very expensive. Thus, the atomistic design was mapped onto a coarse grained system of “beads”. This was accomplished by following MARTINI force field development protocols. The bonded and non- bonded interaction parameters for the system of PMMA-based resin material and TiO2 type nanoparticles were developed and the system was studied extensively to analyze the structure- property correlations which cause the enhancement of properties in case of nanoparticles dispersion. The factors such as, nanoparticles size, volume fraction, polydispersity index, etc. were studied to determine their effects on final nanocomposite structure. This work was later coupled with experimental analysis in order to accomplish three key objectives: (a) to provide validation to the computational modeling work, (b) to study the effect of nanoparticles distribution inside a coating film on the final mechanical properties, and (c) to lead the computational-experimental approach towards a new knowledge discovery and optimization of a “self-cleaning” smart functionality.

It was realized that the optimization of nanocoating formulations solely cannot bring these emerging products into commercialization due to various types of environmental hazards, health concerns and economic issues associated with it. Thus, a comprehensive sustainability assessment framework was developed to look into all the aspects of nanocoating technology, not only at the manufacturing stage, but over its entire life-cycle which includes all the phases from material selection and preprocessing, paint manufacturing, paint application and film formation to its end use and disposal. The developed methodology demonstrated how the sustainability indicators at various stages of life are correlated with each other. This technology can gain significant value and the nanoproducts can be commercialized only if its sustainability

performance is assessed, compared with existing technologies and further improved to make it a safer alternative.

However, the challenge to such detailed assessment emerged from the availability of data for the nanopaint. Thus, Computational Fluid Dynamics (CFD) approach was adopted to simulate these nanopaints and study the coating quality parameters and environmental emission factors during application phase. In automotive industries, paint-spray is the most common application method used for coating the auto bodies. In this research, an industrial paint booth was designed and the spray of various paint systems was studied. The components which may primarily affect the environment during paint spray are VOC (Volatile Organic Contents) and nanoparticles. Through CFD simulation tool, ANSYS FLUENT, the paint spray process was simulated and the emission of VOC‟s and nanoparticles was thoroughly assessed. This simulation generated crucial information related to the concentrations of overspray paint and VOC‟s that was released in the atmosphere and could cause health hazards to the workers inside the spray booth and pollute the drainage water passing through these paint booths. The final coating quality parameters were also studied to ensure the superior performance of nanopaints over conventional paints. The quality parameters such as, film topology, roughness, paint spray efficiency, etc. were determined and the results for different types of conventional and nanopaints were compared.

The sustainability assessment framework and multiscale computational modeling methodologies generated in this research can provide a key pathway towards optimizing an emerging nanocoating technology and justify the inclusion of nanopaint products into future commercial market. This research can also assist experimentalists in learning the structure-

property correlations of nanocomposite coatings and discover novel formulations of these materials for the future.