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In this article we report the production of metal oxide (TiFe2O4, ZnFe2O4) nanoparticles by pulsed laser ablation technique in a liquid environment. We used nanosecond Nd: YAG laser systems working at 532 nm and 1064 nm of wavelength and the energy of the laser beam was kept constant at 80 mJ. Absorbance spectra, surface plasmon resonance, optical band-gap, and nanoparticle morphology were investigated using ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Changing the wavelength of the laser for growth, nanoparticles showed shift between the absorbance and surface plasmon resonance peaks in their UV-Vis spectra, which implies that the optical properties of the colloid nanoparticles depend on laser parameters. This was confirmed with the variation of the band gap energy. Furthermore, redshift for the absorbance peak was observed for samples as-grown at 532 nm around 150 nm as a function of time preparation. Conversely, for the samples as-grown at 1064 nm there was no shift in the absorbance spectra, which could be due to agglomeration and formation of larger particles. The characterization results showed appropriate plasmonic photo-catalysts properties of the particles, hence the photoactivation of the nanoparticles was examined on antibacterial effect using colonies of Staphylococcus aureus and Escherichia coli.

In the following paper, we present a nonlinear model of an atomic force microscope considering the potential of Lennard–Jones and the nonlinear friction produced by the squeeze film damping effect, between the cantilever and the sample. Specifically, we study the existence and stability of periodic solutions using the lower and upper solution method in the system without friction. The condition for persistence of the homocline orbit was established by Melnikov method when the model has nonlinear friction. In this sense, the analytic and numerical approach is presented to verify the solutions of the model.

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Climate change has influenced several of the water cycle related variables such as rainfall that contribute to increasing natural disasters. To establish new methodologies for rivers level forecasting is necessary for the implementation of early warning systems. In this work, we present results of a multilayer perceptron artificial neural network (ANN) to forecast temporal series of water levels at the outlet of Rio Negro river with 24-hour antecedence. Input data was collected by a set of hydrological monitoring stations composed of water level and rainfall measures acquired with a one-day resolution. Water-level prediction were evaluated by the Nash-Sutcliffe coefficient (NSE) and by the root mean square error (RMSE). The results show consistency between predicted and observed values, especially when combining both water level and rainfall data. In such case, values of NSE reached 0.93 to 0.54 and RMSE between 0.028 and 0.061 for antecedence of 1 to 7 days respectively with implemented topology for the empirical model.