Polymer nanocomposite data set for prediction and modeling of transmitted light intensity via machine learning models

Published: 24 March 2022| Version 1 | DOI: 10.17632/xdm7bv4kpw.1


Light transmission from polymers doped with carbon-based nanofillers such as multi-walled carbon nanotubes (MWCNTs) is one of the key challenges in optimal design of conductive nanocomposite materials. Incorporation of MWCNT nanofillers into polymers at high concentrations remarkably improves electrical conductivity of neat, insulating polymers, however; it also leads to lower transmission of photons from heterogeneous medium of resultant nanocomposite since MWCNT nanofillers act as light scatters. In this respect, it is experimentally important to determine the critical concentration of MWCNTs in preserving the optical transparency and electrical conductivity of the material, simultaneously. In our data set, we show transmitted light (photon) intensities of polystyrene (PS) latexes doped with MWCNT nanofillers at different concentrations (i.e mass fractions). Emulsion polymerization experiments were first conducted to align particle size and molecular weight values of latex particles based on initiator and surfactant concentrations used during polymerization reaction. Three different sets of PS latexes, each having different molecular weight and particle diameter, were then mixed with nanofillers at different mass fractions up to 20 wt%. Afterward, each prepared nanocomposite solution was deposited on transparent glass plates by the same amounts of solution droplets to be annealed in oven. Transmitted light intensity of the samples was recorded using UV-Vis spectrophotometer after each annealing step in a broad temperature range varying between 100 and 250 Celsius degree. Each measurement parameter was listed in a separate column and 512 different measurement data (where each row represents one measurement) were collected in total. The first four columns (initiator concentration, surfactant concentration, molecular weight of PS latex and mean particle diameter of PS latex particles) belong to PS latex polymers. The fifth column (MWCNT concentration) represents how much MWCNT nanofillers were added into PS latexes. The last two columns stand for annealing temperature and measured transmitted light intensity values. Our experimental data clearly show that transmitted light intensity of PS/MWCNT nanocomposite films is significantly low in the presence of high MWCNT content and at low annealing temperatures. Our optical data set collected from photon transmission measurements is suitable for studying polymer film physics applying percolation and other statistical theories, material characterization, mathematical modeling and machine learning. Our experimental data set can be of great interest for researchers and computational scientists in developing neural network topologies, support vector regression (SVR), adaptive neuro fuzzy interference system (ANFIS) and other machine learning models combined with many advanced optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC).



Istanbul Teknik Universitesi


Materials Science, Artificial Neural Networks, Polymer Physics, Carbon Nanotubes, Machine Learning, Support Vector Machine, Mathematical Optimization, Multivariate Regression, Emulsion Polymerization, Polymer Nanocomposites, Transmission, Analysis of Variance, Latex Emulsion, Film Coating, Percolation Model, Fuzzy-Neural System