The aim of the present work is to develop a new class of natural fillers based polymer composites with sawdust (S.D) which used two particle sizes (1.2 μm & 2.3 μm) and different weight percentage from sawdust (10%, 15%, and 20%). The mechanical properties studied include hardness (shore D) for all samples at normal conditions (N.C). The unsaturated polyester (UPE) and its composites samples were immersed in water for 30 days to find the effect of particle size of sawdust (S.D) on the weight gain (Mt %) by water for all the samples, also to find the effect of water on their hardness. The results show that the composite materials of sawdust (S.D) fillers which has particle size (1.2 μm) better than (2.3 μm) particle size before & after the immersion in water. Also the results show a decrease in the values of the hardness for the UPE and its composites samples after immersion in water. The results show that the UPE and its composites samples have relatively increased values of weight gain (Mt %) by water with time of immersion, for sawdust composite samples (1.2 μm) particle size the samples of (15%) weight percentage have relatively highest values of weight gain (Mt %) by water but for samples of (2.3 μm) particle size the samples of (20 %) weight percentage have relatively highest values of weight gain (Mt %). Finally results show that the value of weight gain (Mt %) increased with increasing of particle size of sawdust so that the composites samples have highest value of weight gain (Mt %) than UPE sample.
This study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to
In order to reduce the environmental pollution associated with the conventional energy sources and to achieve the increased global energy demand, alterative and renewable sustainable energy sources need to be developed. Microbial fuel cells (MFCs) represent a bio-electrochemical innovative technology for pollution control and a simultaneous sustainable energy production from biodegradable, reduced compounds. This study mainly considers the performance of continuous up flow dual-chambers MFC
fueled with actual domestic wastewater and bio-catalyzed with anaerobic aged sludge obtained from an aged septic tank. The performance of MFCs was mainly evaluated in terms of COD reductions and electrical power output. Results revealed that the C
In this study tungsten oxide and graphene oxide (GO-WO2.89) were successfully combined using the ultra-sonication method and embedded with polyphenylsulfone (PPSU) to prepare novel low-fouling membranes for ultrafiltration applications. The properties of the modified membranes and performance were investigated using Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), contact angle (CA), water permeation flux, and bovine serum albumin (BSA) rejection. It was found that the modified PPSU membrane fabricated from 0.1 wt.% of GO-WO2.89 possessed the best characteristics, with a 40.82° contact angle and 92.94% porosity. The permeation flux of the best membrane was the highest. The pure water permeation f
... Show MoreThis study aims to measure the basic foundations of organizational health in the General Company for Food Products and to indicate the extent of its presence or not within the company under investigation.
This research was completed using a descriptive and analytical approach using a sample of 97 employees from the General Company for Petroleum Products. Calculating the arithmetic mean, standard deviation, coefficient of variation, and confirmatory factor analysis are all part of the data processing process.