Effects of different metal corrosion inhibitors on hydrogen suppression and film formation with Mg–Zn alloy dust in NaCl solutions

To improve the safety of wet dust removal systems for processing magnesium-based alloys, a new method is proposed for preventing hydrogen generation. In this paper, hydrogen generation by Mg–Zn alloy dust was inhibited with six common metal corrosion inhibitors. The results showed that sodium dodecylbenzene sulfonate was the best hydrogen inhibitor, while CeCl₃ enhanced hydrogen precipitation. The film-forming stability of sodium dodecylbenzene sulfonate was tested with different contents, temperatures, Cl^? concentrations and perturbation rates. The results showed that this inhibitor formed stable protective films on the surfaces of Mg–Zn alloy particles, and adsorption followed the Langmuir adsorption model.     

Enhancing sustainable bioelectricity generation using facile synthesis of nanostructures of bimetallic Co–Ni at the combined support of halloysite nanotubes and reduced graphene oxide as novel oxygen reduction reaction electrocatalyst in single-chambered microbial fuel cells

The present study aims to utilize the high surface area of the nanotube structure of halloysite (HNTs), an aluminosilicate clay, and conductivity of reduced graphene oxide (rGO) as support material for the deposition of nickel (Ni) and cobalt (Co) nanoparticles. With that aim, a novel bimetallic cathode electrocatalyst, Co–Ni @ HNTs-rGO (Catalyst H₃), is developed. This catalyst is characterized by X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and Transmission Electron Microscopy (TEM). Catalyst H₃ demonstrates outstanding oxygen reduction reaction (ORR) activity, electrochemical stability, electrocatalytic performance, and lowest resistance in comparison to the other developed catalysts and conventional Pt/C. Catalyst H₃ is used in single-chambered MFCs (microbial fuel cells), where the anode is filled with molasses-laden wastewater. The attained maximum power density in MFC (catalyst H₃) is 455 ± 9 mW/m², which is higher than other catalysts. All the results indicate towards its potential use in MFC application.     

Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power.     

Experimental and numerical study on tube-side flow and heat transfer characteristics of superheated vapor flow in catalyst-filled LH2 spiral wound heat exchanger

High energy consumption is considered to be one of the most persistent problems in liquid hydrogen (LH₂) plants. The combination of heat exchanger and ortho-para (O–P) hydrogen conversion has attracted considerable attention as a cutting-edge technology to reduce energy consumption. The flow and heat transfer characteristics of O–P hydrogen conversion catalyst-filled spiral wound heat exchanger (SWHE) were investigated in this study in two steps. In the first step, pressure-drop experiments were performed in a tube filled with porous media. The results indicated that the pressure drop was overestimated when using Ergun's equation. Therefore, a new empirical pressure-drop correlation for a channel filled with O–P catalyst was formulated. Subsequently, a novel heat transfer model was established based on this correlation for further numerical simulations. The distributions of the temperature, pressure, and para hydrogen content in a catalyst-filled tube were determined. In addition, the influence of the flow rate on the heat exchange coefficient and outlet para hydrogen was clarified; it was found that, with an increase in the flow rate, the heat exchange coefficient increased, whereas the outlet para hydrogen content decreased. At a flow rate of 0.5 m³/h, the para hydrogen content increased by 44% after hydrogen flowed through the channel filled with the O–P catalyst. Furthermore, a prediction model for the para hydrogen content with a flow rate range of 0–1.5 m³/h was derived. This study provides promising theoretical evidence for the engineering application of SWHEs filled with O–P catalysts in large-scale hydrogen liquefaction units.     

ZIF-8 derived bimetallic Fe–Ni-Nanoporous carbon for enhanced oxygen reduction reaction

The development of highly efficient nοn-nοble meta? catalysts for (ОRR) in PEMFCs is at the heart of the research, yet their performance is not satisfactory. The Fe–N active sites enclosed in carbon matrix are generally agreed to be the most promising active sites for ORR. In view of this, further effort is made to increase the Fe–N active sites. Here we present the fabrication of novel FeNi bimetallic electrоcatalyst obtained from ZIF, which consists of FeNi nanоallоys incorporated in N-doped carbon (FeNi-NC) featuring carbon nanotubes and porous carbon demonstrates outstanding results for ОRR. The Fe–N and Ni–N active sites synergistically enhance the ORR activity of FeNi-NC catalyst. The FeNi-NC showed remarkable performance in KОH with the half-wave and onset potential of 0.89 V and 0.99 V vs RHE, respectively. This catalyst shows exceptional stability in methanol equivalent to Pt/C commercial. The FeNi-NC retained 71%, while Pt/C commercial retained only 59% of its original current density. The exceptional stability and activity might be associated with the interplay among FeNi active sites and N-doped carbon, the distinct nanо-structure made up of porous carbon and carbon nanotubes with a high graphitization degree.     

Gyrotactic microorganisms bioconvection in combined convective magnetohydrodynamic Casson nanofluid flow over a vertically surfaced saturated porous media with variable viscosity

The current article focuses on mass and thermal transfer analysis of a two-dimensional immovable combined convective nanofluid flow including motile microorganisms with temperature-dependent viscosity on top of a vertical plate through a porous medium, and a model has been developed to visualize the velocity slip impacts on a nonlinear partial symbiotic flow. The governed equations include all of the above physical conditions, and suitable nondimensional transfigurations are utilized to transfer the governed conservative equations to a nonlinear system of differential equations and obtain numerical solutions by using the Shooting method. Numerical studies have been focusing on the effects of intricate dimensionless parameters, namely, the Casson fluid parameter, Brownian motion parameter, thermophoresis parameter, Peclet number, bioconvection parameter, and Rayleigh number, which have all been studied on various profiles such as momentum, thermal, concentration, and density of microorganisms. The concentration boundary layer thickness and density of microorganisms increased as the Casson fluid parameter, Brownian and thermophoresis parameters increased, whereas the bioconvection parameter, Peclet number, and Rayleigh number increased. The thermal boundary layer thickness, concentration boundary layer thickness, and density of microorganisms all decreased. The velocity distribution decreases as the Peclet number, bioconvection, and thermophoresis parameters rise but rises as the Rayleigh number, Brownian motion parameter, and Casson fluid parameter rise. These are graphed via plots along with divergent fluid parameters.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Icy affairs: Understanding recent advancements in the freezing and frozen storage of fish

Freezing methods have evolved over the last 30 years. This review states the effect of various freezing methods on the quality of fish and seafood. Freezing temperatures, freezing, and frozen storage temperatures were also analyzed and reviewed. The changes in the ice crystal, protein, and lipid affect the fish quality and nutritional value during freezing and frozen storage. Freezing methods when combined with various additives or preprocessing approaches help improve the efficacy of freezing and frozen storage. Several experimental or emerging methods also have positive effects on the products' quality. According to the metadata reanalysis of quality markers, freshly frozen fish using different freezing methods may vary much in terms of ice diameter, but not others. High pressure freezing or immersion freezing-derived fish retains the best quality through frozen storage. More data are required on freezing methods (electrical-assisted freezing, microwave-assisted freezing, magnetic-assisted freezing, radiofrequency-assisted freezing, and the commercial's application and investment should be considered in the future. This review sheds light on finding a balanced initial shear force during freezing and the use of certain additives to control freezing-related damages. Focusing on ice diameter alone may be futile (e.g., liquid N₂ freezing). Future optimization of technologies should be in a way that several processes along the farm to fork such as freezing, frozen storage, thawing, thermal processing of fish, and even refabrication of food should mutually complement each other's needs to deliver safe and high-quality fish to the consumer's plate, even after a prolonged shelf-life.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Experimental evaluation of the luminescence performance of fired clay brick coated with SrAl2O4:Eu/Dy phosphor

This study evaluates the luminescence performance of fired clay bricks coated with SrAl₂O₄:Eu/Dy phosphor. To do so, SrAl₂O₄:Eu/Dy phosphor was first produced using the traditional solid-state reaction synthesis technique. The prepared phosphor was then used for coating fired clay bricks to analyze the luminescence performance via spectral analysis, decay characteristics, and microstructure of the bricks. The results reveal that excitation and emission spectra of the phosphor coated bricks range from 200 to 480 nm and 455 to 650 nm, respectively, suggesting that the phosphor coated bricks have the capacity of absorbing light with a wide range of wavelengths. The peak wavelength projected at 511 nm in the emission spectrum is achieved, which indicates 4f⁶5 d¹-4f⁷ transition of Europium (Eu²⁺). The repeated excitation and deexcitation of Eu²⁺ by using hole traps and trap levels offered by Dysprosium (Dy³⁺), exist between the ground and the excited state of Eu²⁺ leads to luminescent phenomenon. Moreover, the decay characteristics has revealed that phosphor coated bricks can emit light for a considerable amount of time (>8.5 min) upon the removal of the excitation source. The results reveal that phosphor coated bricks has the potential of increasing energy efficiency of residential and commercial buildings.