The development of efficient and environmentally friendly catalysts for the electro-oxidation of hydrazine derivatives is of great importance in various industrial applications. In this study, we report the utilization of graphitebased catalysts for the electro-oxidation of hydrazine derivatives, using sodium chloride as a green and sustainable chemical approach. Graphite, a two-dimensional carbon material with exceptional properties, offers numerous advantages as a catalyst, including its high surface area, excellent electrical conductivity, and chemical stability. These characteristics make graphite an ideal candidate for promoting electrochemical reactions. Sodium chloride (NaCl), a readily available and cost-effective salt, serves as a green alternative to traditional oxidants used in hydrazine oxidation processes. By replacing conventional oxidizing agents with NaCl, we aim to reduce the environmental impact associated with the production and disposal of hazardous chemicals. This process enables the transformation of the HN-NH bond within hydrazines, leading to the formation of azo compounds (N¼N). Azo compounds are important organic molecules with diverse applications in organic synthesis. This novel approach has successfully showcased the efficacy of utilizing various azo compounds in 13 different examples, yielding excellent or moderate to good results. The method capitalizes on electricity as the final oxidizing agent, providing an environmentally friendly oxidation strategy. Its high efficiency and gentle reaction conditions make this technique valuable for synthesizing azo derivatives, even when working with hydrazines containing diverse functional groups, resulting in yields ranging from moderate to excellent. Through systematic experiments, we evaluated the catalytic performance of graphite-based catalysts in the electro-oxidation of hydrazine derivatives. The catalysts demonstrated remarkable catalytic activity due to their efficient conversion of hydrazine derivatives into desired products. Moreover, the system exhibited good stability and recyclability, suggesting its suitability for practical applications.
In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and
... Show MoreFace recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise J
... Show MoreThe effect of different antibiotics on growth pigment and plasmid curing of Serratia marcescens were studied, S. marcescens was cultured in media containing(16_500)µg/ml of antibiotics, curing mutants unable to produce prodigiosin and lost one plasmid band were obtained of of ampicillin, amoxillin, antibiotics concentrations (64 500) µg/ml metheprim, ultracloxam, azithromycin, cephalexin and erythromycin treated with (350 500) µg/ml of The mutant cells rose- light color and and refampicin revealed S.marcescens inhibited ciprodar and tetracyclin, lincomycin did not lost the plasmid band chlaforan
A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s un
... Show MoreThe automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acou
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