A scattered data approximation tool to map single-walled carbon nanotube dispersion to the processing parameters in polymer nanocomposites

The relationship of nanocomposite dispersion to dispersion techniques and other processing parameters was studied. Examining all permutations of the various factors in the laboratory is a challenging task. In this paper, we propose to map a correlation between inputs and output via a self-adaptive scattered data approximation method. The proposed greedy algorithm, sequential function approximation (SFA), reveals the multidimensional behaviour of the system, provides the sensitivity of each input and presents the combination of inputs that is most suitable for a specific output. In this research, we have collected data from various research institutions and applied it to SFA. The results show that SWNT weight percent, sonication time, SWNT modification and high shear mixing time are key factors that affect the dispersion. This text discusses SFA, the data and the results in detail. This work serves as a proof of concept for functional mapping to be applied to polymer processing.