Manufacturing catalyst-coated membranes by ultrasonic spray deposition for PEMFC: Identification of key parameters and their impact on PEMFC performance

This study deals with the manufacturing of catalyst-coated membranes (CCMs) for newcomers in the field of coating. Although there are many studies on electrode ink composition for improving the performance of proton-exchange membrane fuel cells (PEMFCs), there are few papers dealing with electrode coating itself. Usually, it is a know-how that often remains secret and constitutes the added value of scientific teams or the business of industrialists. In this paper, we identify and clarify the role of key parameters to improve coating quality and also to correlate coating quality with fuel cell performance via polarization curves and electrochemical active surface area measurements. We found that the coating configurations can affect the performance of lab-made CCMs in PEMFCs. After the repeatability of the performance obtained by our coating method has been proved, we show that: (i) edge effects, due to mask shadowing - cannot be neglected when the active surface area is low, (ii) a heterogeneous thickness electrode produces performance lower than a homogeneous thickness electrode, and (iii) the origin and storage of platinum on carbon powders are a very important source of variability in the obtained results.     

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.     

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.     

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.     

Gluten–starch interface characteristics and wheat dough rheology—Insights from hybrid artificial systems

Referring to the total surface existing in wheat dough, gluten–starch interfaces are a major component. However, their impact on dough rheology is largely unclear. Common viewpoints, based on starch surface modifications or reconstitution experiments, failed to show unambiguous relations of interface characteristics and dough rheology. Observing hybrid artificial dough systems with defined particle surface functionalization gives a new perspective. Since surface functionalization standardizes particle–polymer interfaces, the impact on rheology becomes clearly transferable and thus, contributes to a better understanding of gluten–starch interfaces. Based on this perspective, the effect of particle/starch surface functionality is discussed in relation to the rheological properties of natural wheat dough and modified gluten–starch systems. A competitive relation of starch and gluten for intermolecular interactions with the network-forming polymer becomes apparent during network development by adsorption phenomena. This gluten–starch adhesiveness delays the beginning of non-linearity under large deformations, thus contributing to a high deformability of dough. Consequently, starch surface functionality affects the mechanical properties, starting from network formation and ending with the thermal fixation of structure.     

Vertex-Edge Degree Based Indices of Honey Comb Derived Network

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry. Chemical reaction network theory is a territory of applied mathematics that endeavors to display the conduct of genuine compound frameworks. It pulled the research community due to its applications in theoretical and organic chemistry since 1960. Additionally, it also increases the interest the mathematicians due to the interesting mathematical structures and problems are involved. The structure of an interconnection network can be represented by a graph. In the network, vertices represent the processor nodes and edges represent the links between the processor nodes. Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks. In this paper, we determined the newly introduced topological indices namely, first -degree Zagreb index, first -degree Zagreb index, second -degree Zagreb index, -degree Randic index, -degree atom-bond connectivity index, -degree geometric-arithmetic index, -degree harmonic index and -degree sum-connectivity index for honey comb derived network. In the analysis of the quantitative structure property relationships (QSPRs) and the quantitative structureactivity relationships (QSARs), graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds. Also, we give the numerical and graphical representation of our outcomes.     

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.