Development of artificial neural networks software for arsenic adsorption from an aqueous environment.

Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation.

A Comparison Study of Fatigue Behavior of Hard and Soft Piezoelectric Single Crystal Macro-Fiber Composites for Vibration Energy Harvesting.

Designing a piezoelectric energy harvester (PEH) with high power density and high fatigue resistance is essential for the successful replacement of the currently using batteries in structural health monitoring (SHM) systems. Among the various designs, the PEH comprising of a cantilever structure as a passive layer and piezoelectric single crystal-based fiber composites (SFC) as an active layer showed excellent performance due to its high electromechanical properties and dynamic flexibilities that are suitable for low frequency vibrations. In the present study, an effort was made to investigate the reliable performance of hard and soft SFC based PEHs.

Effects of microstructure and deformation conditions on the hot formability of Ni-Ti-Hf shape memory alloys.

Ingots of Ni-Ti-Hf shape memory alloys were prepared by vacuum arc re-melting. Isothermal hot compression tests were conducted at temperatures ranging from 700 to 1000 degrees C and at strain rates from 10(-2) s(-1) to 1.0 s(-1).