Synthesis and Thermal Treatment of Pd-Cr@Carbon for Efficient Oxygen Reduction Reaction in Proton-Exchange Membrane Fuel Cells

A nanostructured Pd-Cr catalyst was deposited on a supported carbon surface using the modified borohydride reduction method for the oxygen reduction reaction (ORR) to be utilized as an efficient catalyst in the proton-exchange membrane fuel cell. The crystal structure and feature nanostructure of the Pd-Cr@carbon were established through the use of X-ray powder diffraction (XRD) and transmission electron microscopy (TEM). Meanwhile, its catalytic activity was studied using the cyclic voltammetry and electrochemical polarization techniques. Based on the XRD analysis, it was observed that the Pd phase with the fcc crystal structure was dominant, while the Pd-Cr phase with tetragonal crystal structure was detected only for the as-prepared sample and samples calcined at 573 K. The estimated average crystallite size of the Pd phase increased from 9.66 to 37.54 nm as the calcination temperature increased to 973 K, and the calcination time had a slight effect on the crystallite size. On the other side, the average crystallite size for the formed Pd-Cr phase slightly increased from 43.74 nm for the as-prepared sample to 44.90 nm for the sample calcined at 573 K for 3 h. The TEM examination revealed the uniform distribution of the Pd and Pd-Cr nanoparticles upon the carbon surface. The calcination temperature and time played an important role in controlling the structural and morphology parameters of Pd-Cr@carbon. The adsorption/desorption potentials were found to be dependent on the calcination temperature and time and hence the particle and crystallite sizes. The optimum ORR activity and chemical stability were observed for samples calcined at 773 K for 3 h.

     

Micro milling of titanium alloy Ti-6Al-4V: 3-D finite element modeling for prediction of chip flow and burr formation

Cutting process of titanium alloy Ti-6Al-4V is considered difficult due to chemical affinity between tool and work material, adhesion, built-up edge and burr formation, and tool wear resulting in loss of productivity. Three dimensional (3-D) chip flow together with local field variables such as temperature, elastic/plastic strain, strain-rate and velocity in the shear zones during micro milling process can be predicted using continuum-mechanics based 3-D Finite Element (FE) modelling and simulation of elastic/viscoplastic work material deformations. This paper provides much needed process insight for chip flow, built-up edge and burr formation by using modeling work with experimental validation. Scanning electron microscopic (SEM) observation of the 3-D chip morphology and burrs demonstrate ductile fractured surfaces together with localized instability and failure behaviors. FE simulations are utilized to investigate the effects of micro milling operation i.e. up and down milling and tool edge radius on 3-D chip flow, built-up edge, and 3-D burr formation. Simulated results are compared with measurements of chip morphology, shape, and dimensions together with tool edge condition of built-up edge and chip adhesion yielding to good agreements.     

Application of multi-criteria decision-making methods to identification of soil moisture monitoring sites in an urban catchment in South Australia

When choosing sites for monitoring of soil moisture for hydrological purposes, a suitable process that considers the factors influencing soil moisture level should be followed. In this study, two multi-criteria decision-making (MCDM) methods, the multi-influencing factor (MIF) method and the analytical hierarchy process (AHP) method, were used to identify the optimal soil moisture monitoring (SMM) sites in the Dry Creek Catchment in South Australia. The most representative areas for nine SMM sites were obtained using the MIF method, considering the factors of rainfall, soil type, land use, catchment slope, elevation, and upslope accumulated area (UAA). The AHP method was used to select the optimal sites using the site-specific criteria. 30.3% of the catchment area in the Australian Water Resources Assessment Landscape (AWRA-L) Grid_DC2 can be considered acceptable as representative area with the MIF method. Four potential sites were evaluated for each AWRA-L grid using the relative weights of the site-specific criteria with the AHP method. The Grid_DC2 required two sites that had the highest overall weight chosen with the AHP analysis. The procedure was repeated for the remaining four AWRA-L grids within the study area to select the required SMM sites.     

Capillary Filling in Nanochannels—Modeling,Fabrication, and Experiments

While capillary filling in channels of micrometers scale is experimentally verified to obey Washburn's law well, the speed of capillary filling in nanochannels is noticeably lower than described by Washburn's formula. This article reports the theoretical and experimental results on capillary filling in open-end and closed-end nanochannels. Nanochannels of 45 nm and 80 nm depth, 10 μm width, were etched in silicon and bonded to a glass cover. Experiments on filling of non-electrolytic liquid in silicon nanochannels were carried out. The filling processes were observed and recorded. To estimate the influence of electrokinetics, a mathematical model to calculate the electroviscous effect was established. This model shows that the contribution of the electroviscous effect in the reduction of filling speed is small. This result also agrees well with previous theoretical work on the electroviscous effect. That means that besides the electroviscous effect, there are other phenomena that contribute to the reduction of capillary filling speed in a nanochannel, such as air bubbles formation. Experimental investigation of capillary filling in open-end and closed-end nanochannels with different lengths was performed. The filling processes of ethanol and isopropanol and the behavior of the trapped air were recorded and evaluated. Analytical models based on the continuum assumption were used to evaluate the experimental data. We observed that the filling process consists of two stages. At the initial stage, experimental data agree well with the theoretical model, but with a higher apparent viscosity. In the final stage, condensation of the liquid phase and dissolution of the gas phase lead to total filling of the nanochannel. The observed phenomena are important for understanding the behavior of multiphase systems in nanochannels.     

Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements

The Nile River is considered the main life artery for so many African countries especially Egypt. Therefore, it is of the essence to preserve its water and utilize it very efficiently. Developing inflow-forecasting model is considered the technical way to effectively achieve such preservation. The hydrological system of the Nile River under consideration has several dams and barrages that are equipped with control gates. The improvement of these hydraulic structures’ criteria for operation can be assessed if reliable forecasts of inflows to the reservoir are available. Recently, the authors developed a forecasting model for the natural inflow at Aswan High Dam (AHD) based on Artificial Intelligence (AI). This model was developed based on the historical inflow data of the AHD and successfully provided accurate inflow forecasts with error less than 10%. However, having several forecasting models based on different types of data increase the level of confidences of the water resources planners and AHD operators. In this study, two forecasting model approach based on Radial Basis Function Neural Network (RBFNN) method for the natural inflow at AHD utilizing the stream flow data of the monitoring stations upstream the AHD is developed. Natural inflow data collected over the last 30 years at four monitoring stations upstream AHD were used to develop the model and examine its performance. Inclusive data analysis through examining cross-correlation sequences, water traveling time, and physical characteristics of the stream flow data have been developed to help reach the most suitable RBFNN model architecture. The Forecasting Error (FE) value of the error and the distribution of the error are the two statistical performance indices used to evaluate the model accuracy. In addition, comprehensive comparison analysis is carried out to evaluate the performance of the proposed model over those recently developed for forecasting the inflow at AHD. The results of the current study showed that the proposed model improved the forecasting accuracy by 50% for the low inflow season, while keep the forecasting accuracy in the same range for the high inflow season.     

The Analysis of Corona Quenching in Cylindrical Precipitators Using Charge Simulation

The well-known technique of charge simulation has been adapted to model the electrical characteristic of cylindrical electrostatic precipitators. The study involved the evaluation of the electric field, voltage, and charge density distributions in the presence of mild corona quenching. The problem has been treated differently than the classic solution of Pauthenier by modifying the assumptions used for solving Poisson's equation. A comparative study of the classical and modified solutions is performed. The results of the charge simulation model show good agreement with the suggested modified solution of Poisson's equation.     

Influence of Anodizing Parameters on the Electrochemical Characteristics and Morphology of Highly Doped P-type Porous Silicon

Silicon - Controlling pore structure, e.g. pore diameter, size and distribution of porous silicon (PS) is highly needed for technological applications such as microfluidic operation and filtration...     

Stability analysis of bilinear systems under aperiodic sampled-data control

This note considers the problem of local stability of bilinear systems with aperiodic sampled-data linear state feedback control. The sampling intervals are time-varying and upper bounded. It is shown that the feasibility of some linear matrix inequalities (LMIs), implies the local asymptotic stability of the sampled-data system in an ellipsoidal region containing the equilibrium. The method is based on the analysis of contractive invariant sets, and it is inspired by the dissipativity theory. The results are illustrated by means of numerical examples.     

A new expert system for learning management systems evaluation based on neutrosophic sets

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.