A process of bacterial cellulose gold nanocomposite has been investigated based on experimental work and cited literature. A literature review on the production process is carried out in this study. Bacterial cellulose is a high crystalline fabric material generally used in biomedical applications. A Nanocomposite was made by synthesis from gold and bacterial cellulose. The experimental work includes growing, and isolating bacterial cellulose, preparation of gold Nanoparticles and preparation of Nano composite. Nanoparticle’s formation and adsorption on the cellulose tissue have been observed visually, where a colour change was observed. The predicted particle size for the gold nano
The degradation performance of aqueous solution of pesticide Alachlor has been studied at solar pilot scale plant in two photocatalytic systems: homogeneous photocatalysis by photo-Fenton and heterogeneous photocatalysis with titanium dioxide. The pilot scale system included of compound parabolic collectors specially designed for solar photocatalytic applications, and installed at University of Baghdad, Department of Environmental Engineering back yard. The influence of different concentrations, H2O2 (200-2400 mg/l), Fe+2(5- 30 mg/l) and TiO2 (100-500 mg/l) and their relationship with the degradation efficiency were studied.
The COD removal efficienc
... Show MoreThe adsorption isotherms and kinetic uptakes of Carbon Dioxide (CO2) on fabricated electrospun nonwoven activated carbon nanofiber sheets were investigated at two different temperatures, 308 K and 343 K, over a pressure range of 1 to 7 bar. The activated carbon nanofiber-based on polymer (PAN) precursor was fabricated via electrospinning technique followed by thermal treatment to obtain the carboneous nanofibers. The obtained data of CO2 adsorption isotherm was fitted to various models, including Langmuir, Freundlich, and Temkin. Based on correlation coefficients, the Langmuir isotherm model presented the best fitting with CO2 adsorption isotherms’ experimental data. Raising the equ
Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on
... Show MoreThis research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.
The lethality of inorganic arsenic (As) and the threat it poses have made the development of efficient As detection systems a vital necessity. This research work demonstrates a sensing layer made of hydrous ferric oxide (Fe2H2O4) to detect As(III) and As(V) ions in a surface plasmon resonance system. The sensor conceptualizes on the strength of Fe2H2O4 to absorb As ions and the interaction of plasmon resonance towards the changes occurring on the sensing layer. Detection sensitivity values for As(III) and As(V) were 1.083 °·ppb−1 and 0.922 °·ppb