Pressure and alloying effects on the metal to insulator transition in NiS2-xSex studied by infrared spectroscopy

The metal to insulator transition in the charge-transfer NiS2-xSex compound has been investigated through infrared reflectivity. Measurements performed by applying pressure to pure NiS2 (lattice contraction) and by Se alloying (lattice expansion) reveal that in both cases an anomalous metallic state is obtained. We find that optical results are not compatible with the linear Se-alloying vs pressure-scaling relation previously established through transport, thus pointing out the substantially different microscopic origin of the two transitions.

Combined Raman and infrared investigation of the insulator-to-metal transition in NiS(2-x)Se(x) compounds

Ambient-condition Raman spectra were collected in the strongly correlated NiS(1-x)Se(x) pyrite (0 <= x <= 1.2). Two samples (x = 0 and x = 0.55) were studied as a function of pressure up to 10 GPa, and for the x = 0.55 sample the pressure dependence of the infrared reflectivity was also measured (0-10 GPa). This gave a complete picture of the optical response of that system on approaching the metallic state both by application of pressure and/or by Se alloying, which corresponds to a volume expansion. A peculiar nonmonotonic (V-shaped) volume dependence was found for the quasiparticle spectral weight of both pure and Se-doped compounds. In the x = 0.55 sample the vibrational frequencies of the chalcogen dimer show an anomalous volume dependence on entering the metallic phase. The abrupt softening observed, particularly significant for the Se-Se pair, indicates the relevant role of the softness of the Se-Se bond as previously suggested by theoretical calculations.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.