The present work describes the comparison of the optical and structural properties between CdTe quantum dots (QD) synthesized in water and in the binary solvent (water + glycerin) via one-pot approach synthesis. The optical properties of the nanocrystals obtained with different synthesis parameters were characterized by UV-visible and photoluminescence spectroscopies. The structural chracterization were performed by Fourier transform infrared spectroscopy (FTIR), zeta potential, size-distribution by dynamic light scattering (DLS), X-ray diffraction (XRD), and also by high-resolution transmission electron microscopy (HRTEM). The optical properties of CdTe QD when synthesized in the binary solvent were better, resulting in the increase of photoluminescence quantum yield (Φƒ). The CdTe QD prepared in 120 min, at pH 10.0, in the Cd:Te molar ratio 20:1, using the molar ratio 1:1.5 of Cd:TGA (thioglycolic acid), exhibited a narrow photoluminescence band and enhanced Φƒ for the samples synthesized in a binary solvent in comparison to water solvent (58.4 and 49.5%, respectively).

A neural network procedure to solve inverse chemical kinetic problems is discussed in this work. Rate constants are calculated from the product concentration of an irreversible consecutive reaction: the hydrogenation of Citral molecule, a process with industrial interest. Simulated and experimental data are considered. Errors in the simulated data, up to 7% in the concentrations, were assumed to investigate the robustness of the inverse procedure. Also, the proposed method is compared with two common methods in nonlinear analysis; the Simplex and Levenberg-Marquardt approaches. In all situations investigated, the neural network approach was numerically stable and robust with respect to deviations in the initial conditions or experimental noises.

A methodology for ion-polymer interaction estimation is discussed in the present work. This method is based on the inversion of experimental spin echo NMR data using Hopfield neural network to retrieve transverse relaxation time distributions. The adopted model systems consist of aqueous solutions of poly (ethylene oxide), molar mass 1500, 4000 and 35000 γ mol-1 and sodium nitroprusside (NP) at different concentrations. Dipolar interaction is investigated in this work through the reduction in the transverse relaxation time and increase in the area under the distribution curves of the PEO protons which presented a linear correlation with the NP concentration. Neural network results were compared with the Simplex optimization procedure and experimental NMR values. The proposed methodology is robust, stable, non restrictive in relation to the system and more efficient to handle experimental data.