In this work, porous silicon gas sensor hs been fabricated on n-type crystalline silicon (c-Si) wafers of (100) orientation denoted by n-PS using electrochemical etching (ECE) process at etching time 10 min and etching current density 40 mA/cm2. Deposition of the catalyst (Cu) is done by immersing porous silicon (PS) layer in solution consists of 3ml from (Cu) chloride with 4ml (HF) and 12ml (ethanol) and 1 ml (H2O2). The structural, morphological and gas sensing behavior of porous silicon has been studied. The formation of nanostructured silicon is confirmed by using X-ray diffraction (XRD) measurement as well as it shows the formation of an oxide silicon layer due to chemical reaction. Atomic force microscope for PS illustrates that the p

Electro-chemical Machining is  significant  process to remove metal with using  anodic dissolution. Electro-chemical machining use to removed metal workpiece from (7025) aluminum alloy using Potassium chloride (KCl) solution .The tool used was made from copper. In this present the optimize processes input parameter use are( current, gap and electrolyte concentration) and surface roughness (Ra) as output .The experiments on electro-chemical machining with use current (30, 50, 70)A, gap (1.00, 1.25, 1.50) mm and electrolyte concentration (100, 200, 300) (g/L).  The method (ANOVA) was used to limited the large influence factors affected on surface roughness and found the current was the large influence f

Copper indium disulphide, CuInS2, is a promising absorber material for thin film photovoltaic which has recently attracted considerable attention due to its suitability to reach high efficiency solar cells by using low cost techniques. In this work CuInS2 thin films have been deposited by chemical spray pyrolysis onto glass substrates at ambient atmosphere, using different [Cu]/[In] ratio in the aqueous solutions at substrate temperature 3000C
and different annealing temperatures . Structural and optical properties of CIS films were analyzed by X-ray diffraction, and optical spectroscopy. Sprayed CIS films are polycrystalline with a chalcopyrite structure with a preferential orientation along the 112 direction and no remains of oxides

Abstract-Industrial and urban development has resulted in the spread of plastic waste and the increase in the emissions of carbon dioxide resulting from the cement manufacturing process. The current research aims to produce green (environmentally friendly) concrete by using plastic waste as coarse aggregates in different proportions (10% and 20%) and nano silica sand powder as an alternative to cement in different proportions (5% and 10% by weight). The results showed that compressive strength decreased by 12.10% and 19.23% for 10% and 20% plastic waste replacement and increased by 12.89% and 20.39% for 5% and 10% silica sand replacement respectively at 28 days. Flexural strength decreased by 12.95% and 19.64% for 10% and 20% plastic waste

    In this research, CNRs have been synthesized using pyrolysis of plastic waste(pp) at 1000 ° C for one hour in a closed reactor made from stainless steel, using magnesium oxide (MgO) as a catalyst. The resultant carbon nano rods were purified and characterized using energy dispersive X-ray spectroscopy (EDX), X-ray powder diffraction (XRD). The surface characteristics of carbon rods were observed with the Field emission scanning electron microscopy (FESEM). The carbon was evenly spread and had the highest concentration from SEM-EDX characterization. The results of XRD and FESEM have shown that carbon Nano rods (CNRs) were present in Nano figures, synthesized at 1000 ° C and with pyrolysis temperature 400° C. One of t

Modeling forward kinematics with neural networks allows for efficient handling of nonlinear relationships and realistic error correction in time-critical applications by relying on accurate training data. This paper presents a Multi-Layer Feed-Forward Neural Network (MLFFNN) to solve the forward kinematics of a 3-DOF robot. The proposed MLFFNN consists of 50 hidden neurons and was trained using 628319 samples to find only the position (x, y, z) of the end-effector. Data were generated by MATLAB, assuming an incremental motion of joints. The joint variables ( , , and ) are the inputs of the NN, which outputs the positions of the end effector (x, y, z) calculated using the Denavit-Hartenberg (DH) method. The results demonstrate that t