Co(II) ion was determined by a new, accurate, sensitive and rapid method via a
continuous flow injection analysis (CFIA) with a chemiluminescence reaction based on
the oxidation of Luminol which is loaded on poly acrylic acid gel beads by hydrogen
peroxide in presence of Cobalt (II) ion as a chemiluminescence catalyst. Chemical and
physical parameters were investigated to obtain the best conditions. Linear dynamic
range of Cobalt (II) ion was from 0.1-20.0 μg.ml-1 with a correlation coefficient r =
0.9758, limit of detection (L.O.D) 0.2 ng/sample from the step wise dilution of lowest
concentration in the calibration graph with the percentage relative standard deviation for
3 μg.ml-1 Co(ll) solution is 0.8537% (n=5). The method was applied successfully for the
determination of Co (II) ion in CoMo catalyst type ( K F124- 3E).
The 3D electro-Fenton technique is, due to its high efficiency, one of the technologies suggested to eliminate organic pollutants in wastewater. The type of particle electrode used in the 3D electro-Fenton process is one of the most crucial variables because of its effect on the formation of reactive species and the source of iron ions. The electrolytic cell in the current study consisted of graphite as an anode, carbon fiber (CF) modified with graphene as a cathode, and iron foam particles as a third electrode. A response surface methodology (RSM) approach was used to optimize the 3D electro-Fenton process. The RSM results revealed that the quadratic model has a high R2 of 99.05 %. At 4 g L-1 iron foam particles, time of 5 h, and
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l
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