Enhanced microwave absorption performance of graphene/doped Li ferrite nanocomposites

Progress and Challenges of Ferrite Matrix Microwave Absorption Materials

Intelligent devices, when subjected to multiple interactions, tend to generate electromagnetic pollution, which can disrupt the normal functioning of electronic components. Ferrite, which acts as a microwave-absorbing material (MAM), offers a promising strategy to overcome this issue. To further enhance the microwave absorption properties of ferrite MAM, numerous works have been conducted, including ion doping and combining with other materials. Notably, the microstructure is also key factor that affects the microwave absorption properties of ferrite-based MAM. Thus, this article provides a comprehensive overview of research progress on the influence of the microstructure on ferrite-based MAM. MAMs with sheet and layered structures are also current important research directions. For core-shell structure composites, the solid core-shell structure, hollow core-shell structure, yolk-eggshell structure, and non-spherical core-shell structure are introduced. For porous composites, the biomass porous structure and other porous structures are presented. Finally, the development trends are summarized, and prospects for the structure design and preparation of high-performance MAMs are predicted.

Application of new chitosan 2,4-dihydroxyacetophenone Schiff base @SrFe12O19 nanocomposite for remove of Pb(II) ion from aqueous solution

A new chitosan 2,4-dihydroxyacetophenone Schiff base @SrFe₁₂O₁₉ (Cs-SB@SrFe₁₂O₁₉) nanocomposite was successfully prepared by one-pot reaction and fully characterized for its functional groups, morphology, elemental analysis and thermal behavior by FT-IR, XRD, VSM, DSC, TGA, zeta potential, FE-SEM and EDS techniques. The VSM result showed that Cs-SB@SrFe₁₂O₁₉ has M_s of 11.81 emu/g and H_c of 5488 Oe, known as hard magnetic material. Finally, the as-prepared sample utilized as a new sorbent for the removal of Pb(II) ions from aqueous solution by using batch adsorption experiments. The adsorption of Pb(II) was carried out at different pH, contact time and initial dose of Cs-SB@SrFe₁₂O₁₉. The maximum adsorption capacity was found to be 132 mg/g (99 %) at pH 5 and the contact time of 120 min. Finally, the kinetic studies reveals that the adsorption process of Cs-SB@SrFe₁₂O₁₉ followed by the pseudo second order kinetics model. Also, the sample showed excellent recyclable efficiency up to 5 cycles.

Employing computational fluid dynamics technique for analyzing the PACK-1300XY with methanol and isopropanol mixture

In this study, an innovative wire gauze structured packing, namely PACK-1300XY with a specific surface area of 1300 m²/m³ has been characterized by performing computational fluid dynamics (CFD) approach. Indeed, different features of this packing (height equivalent to a theoretical plate, wet/dry pressure drop, and mass transfer efficiency) were analyzed by analyzing the flow regime using the three-dimensional CFD approach with the Eulerian-Eulerian multiphase scenario. The results showed the mean relative deviation of 16% (for wet pressure drop), 14% (for dry pressure drop), and 17% (for mass transfer efficiency) between the CFD predictions and experimental measurements. These excellent levels of consistency between the numerical findings and experimental observations approve the usefulness of the CFD-based approach for reliable simulation of separation processes.

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.