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. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).

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. The base acceleration of both PEHs is held at 7 m/s² and the frequency of excitation is tuned to their resonant frequency (f_r) and then the output power (P_rms) is monitored for 10⁷ fatigue cycles. The effect of fatigue cycles on the output voltage, vibration displacement, dielectric, and ferroelectric properties of PEHs was analyzed. It was noticed that fatigue-induced performance degradation is more prominent in soft SFC-based PEH (SS-PEH) than in hard SFC-based PEH (HS-PEH). The HS-PEH showed a slight degradation in the output power due to a shift in f_r, however, no degradation in the maximum power was noticed, in fact, dielectric and ferroelectric properties were improved even after 10⁷ vibration cycles. In this context, the present study provides a pathway to consider the fatigue life of piezoelectric material for the designing of PEH to be used at resonant conditions for long-term operation.

The Role of Nano-domains in {1-011} Twinned Martensite in Metastable Titanium Alloys

The formation mechanism of [Formula: see text] type twinned α'-martensitic structures was investigated in titanium alloys, and in-depth characterizations of the microstructures were performed using scanning electron microscopy and transmission electron microscopy. The randomly distributed nano-domains nucleated by water quenching were sheared during the primary martensite transformation. Experimental results revealed that this sheared nano-domain interferes with the primary martensite transformation and induces a secondary martensite transformation. In terms of crystallography, the secondary martensite transformed from the sheared nano-domain has a [Formula: see text] type twin relationship with the primary martensite. The growth of both martensites yielded a more twinned martensitic structure as the applied strain increased.