Titanium surface topography after brushing with fluoride and fluoride-free toothpaste simulating 10 years of use

Objectives: To conduct a controlled study contrasting titanium surface topography after procedures that simulated 10 years of brushing using toothpastes with or without fluoride. Methods: Commercially pure titanium (cp Ti) and Ti-6Al-4V disks (6 mm circle divide x 4 mm) were mirror-polished and treated according to 6 groups (n = 6) as a function of immersion (I) or brushing (B) using deionised water (W), fluoride-free toothpaste (T) and fluoride toothpaste (FT). Surface topography was evaluated at baseline (pretreatment) and post-treatment, using atomic force microscope in order to obtain three-dimensional images and mean roughness. Specimens submitted to immersion were submerged in the vehicles without brushing. For brushed specimens, procedures were conducted using a linear brushing machine with a soft-bristled toothbrush. Immersion and brushing were performed for 244 h. IFT and BFT samples were analysed under scanning electron microscope with Energy-Dispersive X-ray Spectroscopy (EDS). Pre and post-treatment values were compared using the paired Student T-test (alpha = .05). Intergroup comparisons were conducted using one-way ANOVA with Tukey post-test (alpha = .05). Results: cp Ti mean roughness (in nanometers) comparing pre and post-treatment were: IW, 2.29 +/- 0.55/2.33 +/- 0.17; IT, 2.24 +/- 0.46/2.02 +/- 0.38; IFT, 2.22 +/- 0.53/1.95 +/- 0.36; BW, 2.22 +/- 0.42/3.76 +/- 0.45; BT, 2.27 +/- 0.55/16.05 +/- 3.25; BFT, 2.27 +/- 0.51/22.39 +/- 5.07. Mean roughness (in nanometers) measured in Ti-6Al-4V disks (pre/post-treatment) were: IW, 1.79 +/- 0.25/2.01 +/- 0.25; IT, 1.61 +/- 0.13/1.74 +/- 0.19; IFT, 1.92 +/- 0.39/2.29 +/- 0.51; BW, 2.00 +/- 0.71/2.05 +/- 0.43; BT, 2.37 +/- 0.86/11.17 +/- 2.29; BFT, 1.83 +/- 0.50/15.73 +/- 1.78. No significant differences were seen after immersions (p > .05). Brushing increased the roughness of cp Ti and of Ti-6Al-4V (p < .01); cp Ti had topographic changes after BW, BT and BFT treatments whilst Ti-6Al-4V was significantly different only after BT and BTF. EDS has not detected fluoride or sodium ions on metal surfaces. Conclusions: Exposure to toothpastes (immersion) does not affect titanium per se; their use during brushing affects titanium topography and roughness. The associated effects of toothpaste abrasives and fluorides seem to increase roughness on titanium brushed surfaces. (C) 2012 Elsevier Ltd. All rights reserved.

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.