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Exposure to hydroquinone (HQ) can cause various health hazards and negative impacts on the environment. Therefore, we developed an efficient electrochemical sensor to detect and quantify HQ based on palladium nanoparticles deposited in a porous silicon-polypyrrole-carbon black nanocomposite (Pd@PSi−PPy−C)-fabricated glassy carbon electrode. The structural and morphological characteristics of the newly fabricated Pd@PSi−PPy−C nanocomposite were investigated utilizing FESEM, TEM, EDS, XPS, XRD, and FTIR spectroscopy. The exceptionally higher sensitivity of 3.0156 μAμM−1 cm−2 and a low limit of detection (LOD) of 0.074 μM were achieved for this innovative electrochemical HQ sensor. Applying this novel modified electrode, we could detect wide-ranging HQ (1–450 μM) in neutral pH media. This newly fabricated HQ sensor showed satisfactory outcomes during the real sample investigations. During the analytical investigation, the Pd@PSi−PPy−C/GCE sensor demonstrated excellent reproducibility, repeatability, and stability. Hence, this work can be an effective method in developing a sensitive electrochemical sensor to detect harmful phenol derivatives for the green environment.

The present study deals with the petrographical, mineralogical and geochemical characteristics of the alteration facies zones recognized around the shear zone at Gabal El-Missikat area, Central Eastern Desert, Egypt. Petrographically, the fresh granitic samples are composed mainly of quartz, K-feldspars (microcline and microcline perthite), plagioclase, biotite. The secondary minerals are sericite, kaolinite, muscovite, chlorite and epidote as well as zircon, apatite, fluorite, titanite and iron oxides as accessory minerals. Two alteration facies zones are recognized and namely as propylitic and advanced argillic. The propylitic facies zone is composed mainly of sericite with minor kaolinite, muscovite, quartz, relics of plagioclases, chlorite and rare epidote as well as zircon, hematite, goethite, magnetite, ilmenite, ilmenorutile, rutile, titanite, apatite, columbite and fluorite and secondary uranium minerals. the advanced argillic facies zone is composed mainly of kaolinite with minor sericite, quartz, muscovite, chlorite and rare epidote as well as zircon, hematite, goethite, magnetite, ilmenite, ilmenorutile, rutile, titanite, apatite and garnet of spessartine type as accessory minerals. The identified minerals in the studied two alteration facies zones can be grouped into three mineral groups which are: the primary minerals (pyrite, magnetite, galena, columbite and gold), the secondary minerals (uranophane, kasolite and wulfenite) and the gangue minerals (anhydrite, barite, celestine, hematite, goethite and fluorite). The identified mineral assemblage of the studied propylitic alteration facies zone may be attributed to strongly alkaline hydrothermal solutions at pH value of more than 7 with temperature varying between 350 and 450ºC, while the advanced argillic alteration facies zone is essentially associated with strongly acidic hydrothermal solutions at pH value less than 7 with temperature varying between 150 and 400ºC.

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Adequate and accurate models of thermal power plants (TPP) and hydropower plants (HPP) are of great importance for the optimal economic functioning of electric power systems. The extracted models have a direct impact on economic dispatch calculations. In this research, a modified optimization algorithm called an improved gradient-based optimizer (IGBO) is deployed for the optimal extraction of TPP and HPP input-output parameters. Firstly, the IGBO is tested for use with well-known benchmark functions and shows outstanding performance over the original GBO and other competitive algorithms. For the input-output parameters extraction of TPP and HPP, the sum of the absolute error (SAE) is utilized as a fitness function to be minimized. Secondly, nine models of TPP and HPP are employed for parameter identification using IGBO. Simulation outcomes prove the capability of IGBO to accurately extract input-output parameters of TPP and HPP. Moreover, the convergence characteristics of IGBO are remarkable among investigated optimization algorithms.