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Photoelectron Diffraction (PED) is an experimental technique widely used to perform structural determination of solid surfaces. Similarly to Low Energy Electron Diffraction (LEED), the structural determination by PED requires a fitting procedure between the experimental intensities and theoretical results obtained through simulations. Multiple scattering has been shown to be an effective approach to making such simulations. The quality of the fit can be quantified through the socalled R-factor. Therefore, the fitting procedure is, indeed, an R-factor minimization problem. However, the topography of the R-factor as a function of the structural and non-structural surface parameters to be determined is complex, and the task of finding the global minimum becomes tough, particularly for complex structures in which many parameters have to be adjusted. In this work we investigate the applicability of the genetic algorithm (GA) global optimization method to this problem. The GA is based on the evolution of species, and makes use of concepts such as crossover, elitism and mutation to perform the search. We show results of its application in the structural determination of three different systems: the Cu(111) surface through the use of energy-scanned experimental curves, the Ag(110)-c(2x2)-Sb system where a theory-theory fit was performed, and the Ag(111) surface for which angle-scanned

Rigid polyurethane foams were synthesized using a renewable polyol from the simple physical mixture of castor oil and crude glycerol. The effect of the catalyst (DBTDL) content and blowing agents in the foams’ properties were evaluated. The use of physical blowing agents (cyclopentane and n-pentane) allowed foams with smaller cells to be obtained in comparison with the foams produced with a chemical blowing agent (water). The increase of the water content caused a decrease in density, thermal conductivity, compressive strength, and Young’s modulus, which indicates that the increment of CO2 production contributes to the formation of larger cells. Higher amounts of catalyst in the foam formulations caused a slight density decrease and a small increase of thermal conductivity, compressive strength, and Young’s modulus values. These green foams presented properties that indicate a great potential to be used as thermal insulation: density (23–41 kg·m−3), thermal conductivity (0.0128–0.0207 W·m−1·K−1), compressive strength (45–188 kPa), and Young’s modulus (3–28 kPa). These biofoams are also environmentally friendly polymers and can aggregate revenue to the biodiesel industry, contributing to a reduction in fuel prices.