Prediction Of Polyacrylate Waterborne Coating Properties And Multi-scale Modeling Of Water Resistance

Paper mainly discussed the properties of polyacrylate waterborne coating and the water resistance of it. The property of water resistance is a key index of the application of waterborne coating. Materials which had poor water resistances, the water molecules in the environment were easily eroded them, and then changed the internal structures, and change some other properties in further. In order to improve coatings water resistances, it was needed to modify the materials in many aspects. However, the method which was most used was still single factor rotation. Because there were so many factors would affect the water resistance, it was a very complicated work for choosing a better formula. Developing a method for predicting the water-resistant of coating quickly and accurately It became an important task for developing a quickly and accurately method to predict water-resistant of waterborne coatings. In the paper, artificial neural networks, molecular dynamics, multi-scale modeling and other methods were used for studying the water resistance of coating.In the second chapter, by the method of artificial neural network simultaneous prediction, the properties of coatings were predicted by the formula. The network model established which includes five input nodes and four output nodes. The five input nodes corresponding to the amounts of three kinds of coating monomers (butyl acrylate, methyl methacrylate and styrene) and two kinds of pigments (TiO2 and CaCO3). The four output nodes corresponding to the four types of coating properties, which were hardness, adhesion, impact resistance and reflectivity. After data preprocessing, the choice of hidden layer nodes, options of the hidden layers number, learning rate and transfer function and other works, the optimal network structure were confirmed. Base on the structure of network, the weights and thresholds values were trained by the experiments results. Then 9 samples'properties were predicted by the network and compared with the measured values. The final prediction accuracies of four properties were calculated. Adhesion and impact resistance have the perfect results. The errors of reflectivity and hardness were 0.16% and 8.02% respectively. The results showed that, BP neural network prediction was a feasible method for predicting the coatings'properties. It could be used to guide the experiments and productions.In Chapter III, saturated water absorption which was a key index in the study of coatings was studied by experiments and simulations together. The discussion was focused on the affect materials ratio on saturated water absorption, and the binding energies between water molecules and materials were calculated. The relationship between saturated water absorption and binding energies was confirmed. At the same time, the hydrogen bonds energies of water molecules in the materials were calculated. And the diffusion coefficients of water molecules which were affected by the hydrogen bonds energies were simulated by the method of molecular dynamics (MD). The results confirmed that the diffusion coefficients were affected by the monomer ratio closely. Increasing the ratio of hydrophilic monomers, the water molecules diffusion coefficient would decrease, and the saturated water absorption would increase. Because of this relationship, a parameter of aqueous functionality of system (φ) was proposed which was stood for the proportion of hydrophilic groups in the total amount of monomer. Then a prediction model of saturated water absorption was built up by diffusion coefficient, aqueous functionality and theirs interaction. The average error of this model was about 5%. It proved that the model could be used to predict the saturated water absorption which would be used in the final multi-scale model of membrane water resistance.In Chapter IV, pigments which played an important role in the coatings were discussed. The wetting effects between kinds of pigments and latexes were considered. By the experiments, the contact angles of pigments and latexes were measured. Then the energies of interfaces between pigments and latexes were calculated by MD method. By the comparison, the relationship between two energies was founded. So the water resistance indexes of pigments were defined which were stood for the properties of pigments. And the indexes were used in Chapter V and Chapter VI.In Chapter V, a multi-scale modeling of water resistance was established base on Fick's second law for measuring the water resistance of membranes quantitatively. Two indexes, balance time (tB) and seepage velocity (SV) were proposed for the characterization of membrane water resistances. Macro-scale factors such as such as the thickness of membranes (H), the ambient temperature (T) and monomer ratio were picked out for discussion. Micro-scale variables, such as water diffusion coefficient (D), membrane saturation water absorption (CB0) and of water molecules escape concentration (CB1) which were affected by the macro-scale factors and influenced the water resistance indexes such as tB and SV. The agreement between the experimental results and calculated results proved the feasibility and reliability. The two indexes could be used for measuring the membranes water resistances, and could guide the producing in further.In Chapter VI, a visual system was programmed combing the discussion in Chapter III, IV and V. By the operations of click and select, the water resistance indexes of coatings which had different monomers ratios, different macro-scales parameters, different kinds and amount of pigments could be calculated quickly. Researchers could use this software design the better coatings with higher water resistance.