Three-dimensional and anisotropic numerical analysis of a PEM fuel cell

In this study, the effects of cell temperature and relative humidity on charge transport parameters are numerically analyzed. In order to perform this analysis, three-dimensional and anisotropic numerical models are developed. The numerical models are integrated into the experimental values for anisotropic electrical conductivities, as depending on cell temperature and relative humidity, that were obtained from our previous study. The achieved results indicate that the values of current densities in the in-plane direction increase with increasing cell temperature and relative humidity, while the current densities reach a maximum in the rib regions for both the numerical model at the through-plane direction. The behaviors of electrolyte potentials are similar with changes in the cell temperature and relative humidity. In addition, the cathode electrical potentials in both the in-plane direction and through-plane direction do not change to a considerable amount with increasing cell temperature and relative humidity.     

Development of high pressure membraneless alkaline electrolyzer

A technology for cyclic generation of hydrogen and oxygen using electrodes made of variable valency material that does not need the use of separating ion-exchange membranes is presented. The technological solution enables to fabricate electrolyzers for uninterrupted producing high-pressure hydrogen with reduced energy intensity of the production. The total work for compressing 1 m³ of hydrogen and 0.5 m³ of oxygen has been estimated. Results of investigation of influence of discrete supply of DC current to the electrolysis cell, in order to improve the processes of gas evolution and to simplify the power systems of the electrolysis plant, have been considered. There is also considered an electrolysis installation equipped with a thermosorption compressor in which LaNi₅ is used as a hydride-forming compound. The comparative characteristics of the developed electrolyzer and the currently used hydrogen generators are given.     

Noncentrosymmetric Tetrel Pnictides RuSi4P4 and IrSi3P3: Nonlinear Optical Materials with Outstanding Laser Damage Threshold

Noncentrosymmetric (NCS) tetrel pnictides have recently generated interest as nonlinear optical (NLO) materials due to their second harmonic generation (SHG) activity and large laser damage threshold (LDT). Herein nonmetal-rich silicon phosphides RuSi₄P₄ and IrSi₃P₃ are synthesized and characterized. Their crystal structures are reinvestigated using single crystal X-ray diffraction and ²⁹Si and ³¹P magic angle spinning NMR. In agreement with previous report RuSi₄P₄ crystallizes in NCS space group P1, while IrSi₃P₃ is found to crystallize in NCS space group Cm, in contrast with the previously reported space group C2. A combination of DFT calculations and diffuse reflectance measurements reveals RuSi₄P₄ and IrSi₃P₃ to be wide bandgap (E_g) semiconductors, E_g = 1.9 and 1.8 eV, respectively. RuSi₄P₄ and IrSi₃P₃ outperform the current state-of-the-art infrared SHG material, AgGaS₂, both in SHG activity and laser inducer damage threshold. Due to the combination of high thermal stabilities (up to 1373 K), wide bandgaps (≈2 eV), NCS crystal structures, strong SHG responses, and large LDT values, RuSi₄P₄ and IrSi₃P₃ are promising candidates for longer wavelength NLO materials.     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Constructing magnetically recoverable CdS/CaFe2O4 P–N heterojunctions for enhanced photoelectrochemical properties towards H2 evolution reaction

A novel CdS/CaFe₂O₄ (CS/CFO) heterogeneous p-n junction was created by thermal deposition of CaFe₂O₄ nanoparticles on CdS rods. The CS/CFO hetero-structured photocatalysts exhibited increasingly efficient visible light harvesting compared to the bare CdS. The CS/CFO composites also presented higher photocurrent and slower decay of photoluminescence, suggesting a better separation of the photo-generated electrons and holes. The photocatalytic H₂ evolution quantity on the optimized CS/CFO composite from water in the presence of ethanol was up to 2200 μmol after 3-h visible light illumination, which is more than twice that of the pristine CdS. The chemical interaction between CdS and CaFe₂O₄ was confirmed by the shifts in the XPS peaks, which made it possible for the charge carriers to transfer across the p-n junction interface. This research highlights the importance of forming an interfacial p-n heterojunction between two semiconductors for efficient charge separation and improved photocatalytic performance.     

Synthesis,characterization and charge transport properties of Pr–Ni Co-doped SrFe2O4 spinel for high frequency devices applications

Ferrites are materials of interest due to their broad applications in high technological devices and a lot of research has been focused to synthesize new ferrites. In this regard, an effort has been devoted to synthesize spinel Pr–Ni co-substituted strontium ferrites with a nominal formula of Sr_1-xPr_xFe_2-yNi_yO₄ (0.0 ≤ x ≤ 0.1, 0.0 ≤ y ≤ 1.0). The cubic structure of pure and Pr–Ni co-substituted strontium ferrite samples calcinated at 1073 K for 3 h has been confirmed through X-ray diffraction (XRD). Average sizes of crystallites (18–25 nm) have been estimated from XRD analysis and nanometer particle sizes of synthesized ferrites have been further verified by scanning electron microscopy (SEM). SEM results have also shown that particles are mostly agglomerated and all the samples possess porosity. It has been observed that at 298 K, the values of resistivity (ρ) increase, while that of AC conductivity, dielectric loss, and dielectric constants decrease with increasing amounts of Pr³⁺ and Ni²⁺ ions. The values of dielectric parameters initially decrease with frequency and later become constant and can be explained on the basis of dielectric polarization. Electrochemical impedance spectroscopy (EIS) studies show that the charge transport phenomenon in ferrite materials is mainly controlled via grain boundaries. Overall, synthesized ferrite materials own enhanced resistivity values in the range of 1.38 × 10⁹–1.94 × 10⁹ Ω cm and minimum dielectric losses, which makes them suitable candidates for high frequency devices applications.     

Synthesis,characterization, and visible-light photocatalytic activity of transition metals doped ZTO nanoparticles

Transition metals doping has been proved to be a feasible way for tuning the physical properties on the surface and bulk of nanomaterials and also for the good performance in decontamination of emerging pollutants. In this context, doped samples of zinc tin oxide or zinc stannate nanoparticles (ZTO NPs) by several transition metals were synthesized in order to enhance the optical absorbance with the aims of reducing the band gap and therefore ameliorated their photocatalytic activity. They were characterized by the X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), diffuse reflectance spectroscopy, Raman spectroscopy and photoluminescence. The XRD patterns and the microscopic observations showed the formation of spherical nanoparticles with an average size of about 30 nm and highly pure ZTO phase with an inverse spinel structure. The Raman spectra were dominated by bands relatives to the F2g (2) and A1g symmetries modes of inverse spinel structure. The band gap Eg is estimated to be 3.75 eV for the undoped sample, and 3.67, 3.64, 3.78 and 3.21 eV, for 2% Fe, 2% Mg, 2% Gd, and 2% Mn doped ZTO samples, respectively.Furthermore, the undoped ZTO NPs have the intrinsic problem of recombination of photogenerated charge carriers. We have shown that the reduction of the band gap and oxygen vacancies resulting from the doping effect could be a useful tool for trapping and avoid the recombination of electrons coming from photosensitized rhodamine B (RhB) under visible light irradiation. Owing to the structural advantages and low band gap, 2% Mn doped ZTO NPs, with the kinetic rate constants k of 0.024 min⁻¹, show enhanced performance for the elimination of RhB in aqueous solution compared to undoped and other doped ZTO NPs.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Automatic generation of variants depending on changes of product properties in a flexible manufacturing environment

Competition forces manufacturing systems to be flexible and to increase product variety and process complexity. These tasks depend on the flexible design of a bill of materials (BOM), one of the most important inputs in manufacturing planning and control systems. Product variety forces systems to generate BOMs with regard to product properties through a BOM pattern. A variant bill of materials provides a structure to manage product variability. In this study, an algorithm is designed to build a BOM pattern using computer-aided design and computer-aided manufacturing (CAD/CAM) data, and another algorithm is designed to generate variants with regard to product specifications. Genetic algorithm is used to generate new products to provide high product variability for testing algorithms. After the test, both algorithms are applied to a real industry problem. The BOM pattern is built automatically using CAD/CAM data, and variants are generated with regard to the pattern, and the results are discussed.