A DFT investigation of lithium adsorption on graphenes as a potential anode material in lithium-ion batteries.

We present a detailed study of the Li+ ion adsorption on two different hydrogenated carbon nanostructures, namely as pristine graphene (PG) and topologic Stone-Wales defective graphene (SWG) using the density functional theory (DFT). The studies are focused to analyze the structure-stability relationship with the estimated electronic and electrical properties for lithium-ion batteries (LIB) formed with an anode based on the Li/Li+#PG and Li/Li+#SWG systems. In addition, the electronic effects induced due to Li+ adsorption and the presence of SW defect on the graphene models were analyzed by the frontier molecular orbitals, ChelpG charges, Raman and UV-Vis spectra. It was verified that Li+ is more stably adsorbed on the edges on both graphene structures through an electrostatic interaction between cation and more negatively charged edges of nanostructures. TD-DFT calculations showed that the metallic nature of isolated graphene is disturbed after the adsorption of Li+, and this was demonstrated from the calculated HOMO-LUMO gap. The same Li+-Graphene geometries were optimized by introducing neutral charge in order to enable the calculation of ionization potentials. I was also found that such systems potentially contributed to the modeling of graphene-based anodes with reasonable electrical voltage responses estimated for a LIB. The simulation of Raman and UV-Vis spectra revealed significant variations in intensity and shifts the typical bands of graphene due to the presence of the Li+ ion that can contribute to point out new experiments to the spectroscopic characterization of these systems. Our results suggest that these carbon nanostructures are potential candidates for efficient applications in electrochemical systems, mainly dealing with LIB.

A genetic algorithm survey on closed-shell atomic nitrogen clusters employing a quantum chemical approach.

The DFT potential energy hypersurfaces of closed-shell nitrogen clusters up to ten atoms are explored via a genetic algorithm (GA). An atom-atom distance threshold parameter, controlled by the user, and an "operator manager" were added to the standard evolutionary procedure. Both B3LYP and PBE exchange-correlation functionals with 6-31G basis set were explored using the GA. Further evaluation of the structures generated were performed through reoptimization and vibrational analysis within MP2 and CCSD(T) levels employing larger correlation consistent basis set. The binding energies of all stable structures found are calculated and compared, as well as their energies relative to the dissociation into N2, [Formula: see text] and [Formula: see text] molecules. With the present approach, we confirmed some previously reported polynitrogen structures and predicted the stability of new ones. We can also conclude that the energy surface profile clearly depends on the calculation method employed.

Growth analysis of sodium-potassium alloy clusters from 7 to 55 atoms through a genetic algorithm approach.

The potential energy hypersurface associated with sodium-potassium alloy clusters is explored via an enhanced genetic algorithm, where two different operators are added to the standard evolutionary procedure. Based on the recent result that the empirical Gupta many-body potential yields reasonable results for clusters with more than seven atoms, we have employed this function in the evaluation of the energies. Agglomerates from seven to the well-established 55-atom structure are studied, and their second-order energy difference and excess energies are calculated. It is found that the most stable alloys (compared to the homonuclear counterparts) are found with the proportion of sodium atoms in the range of 30 to 40%. The experimental propensity of core-shell segregation is successfully predicted by the current approach.

Electronic Quenching in N((2)D) + N2 Collisions: A State-Specific Analysis via Surface Hopping Dynamics.

The electronic quenching reaction N((2)D) + N2 → N((4)S) + N2 is studied using the trajectory surface hopping method and employing two doublet and one quartet accurate potential energy surfaces. State-specific properties are analyzed, such as the dependence of the cross section on the initial quantum state of the reactants, vibrational energy transfer, and rovibrational distribution of the product N2 molecule in thermalized conditions. It is found that rotational energy on the reactant N2 molecule is effective in promoting the reaction, whereas vibrational excitation tends to reduce the reaction probability. For initial states and collision energy thermalized in an initial bath, it is found that the products are "hotter", both vibration and rotation wise.

Experimental and theoretical studies of the thermal behavior of titanium dioxide-SnO2 based composites.

In this paper we report experimental and theoretical studies concerning the thermal behavior of some organotin-Ti(IV) oxides employed as precursors for TiO(2)/SnO(2) semiconducting based composites, with photocatalytic properties. The organotin-TiO(2) supported materials were obtained by chemical reactions of SnBu(3)Cl (Bu = butyl), TiCl(4) with NH(4)OH in ethanol, in order to impregnate organotin oxide in a TiO(2) matrix. A theoretical model was developed to support experimental procedures. The kinetics parameters: frequency factor (A), activation energy, and reaction order (n) can be estimated through artificial intelligence methods. Genetic algorithm, fuzzy logic, and Petri neural nets were used in order to determine the kinetic parameters as a function of temperature. With this in mind, three precursors were prepared in order to obtain composites with Sn/TiO(2) ratios of 0% (1), 15% (2), and 30% (3) in weight, respectively. The thermal behavior of products (1-3) was studied by thermogravimetric experiments in oxygen.

Analyses of the temperature and pH effects on the complexation of magnesium and calcium in human blood plasma: an approach using artificial neural networks.

The temperature and pH effects on the equilibrium of a blood plasma model have been studied on the basis of artificial neural networks. The proposed blood plasma was modeled considering two important metals, calcium and magnesium, and six ligands, namely, alanate, carbonate, citrate, glycinate, histidinate and succinate. A large data set has been used to simulate different concentrations of magnesium and calcium as a function of temperature and pH and these data were used for training the neural network. The proposed model allowed different types of analyses, such as the effects of pH on calcium and magnesium concentrations, the competition between calcium and magnesium for ligands and the effects of temperature on calcium and magnesium concentrations. The model developed was also used to predict how the variation of calcium concentration can affect magnesium concentrations. A comparison of neural network predictions against experimental data produced errors of about 3%. Moreover, in agreement with experimental measurements (Wang et al. in Arch. Pathol. 126:947-950, 2002; Heining et al. in Scand. J. Clin. Lab. Invest. 43:709-714, 1983), the artificial neural network predicted that calcium and magnesium concentrations decrease when pH increases. Similarly, the magnesium concentrations are less sensitive than calcium concentrations to pH changes. It is also found that both calcium and magnesium concentrations decrease when the temperature increases. Finally, the theoretical model also predicted that an increase of calcium concentrations will lead to an increase of magnesium concentration almost at the same rate. These results suggest that artificial neural networks can be efficiently applied as a complementary tool for studying metal ion complexation, with especial attention to the blood plasma analysis.

Controlled release of rhodium (II) carboxylates and their association complexes with cyclodextrins from hydroxyapatite matrix.

Preparation and characterization of a controlled release system of rhodium (II) citrate, acetate. propionate, butyrate and their inclusion or association compounds with cyclodextrin (CD) are described. The porous hydroxyapatite (HA) was characterized by X-ray powder pattern diffraction, FTIR and solid state 31P NMR. Scanning electron microscopy and gas adsorption analysis (BET) were also performed. Release profiles of rhodium (II) carboxylates and their inclusion or association compounds from HA matrix were obtained at different drug loadings (5% and 10%). These were reasonably consistent with a diffusion model. This analysis, mainly using rhodium (II) citrate and butyrate, showed that the strategy of using CDs with a HA matrix may offer a useful new method for the controlled release of these compounds, and hence an alternative strategy for the controlled release of chemotherapeutic agents containing toxic metals. This may be a valuable new technique for localized anti-tumour chemotherapy that minimizes the side effects of such agents.