Electrical and magnetic properties of double perovskite: Y2CoMnO6

In this communication, the structural, micro-structural, dielectric, electrical, magnetic, and leakage-current characteristics of a double perovskite (Y₂CoMnO₆) ceramic material have been reported. The material was synthesized via a high-temperature mixed-oxide route. The compound crystallizes in a monoclinic structure which is confirmed from preliminary X-ray structural study. The morphological study by using scanning electron micrograph reveals the almost homogeneous distribution of grains throughout the surface of the sample. The nature of frequency-dependence of dielectric constant has been described by the Maxwell-Wagner model. The occurrence of a dielectric anomaly in the temperature dependence of dielectric permittivity study demonstrates the ferroelectric-paraelectric phase transition in the material. From the Nyquist plots, we found the existence of both grain and grain boundary effects. The frequency dependence of conductivity was studied by the Jonscher’s Power law, and the conduction phenomenon obeys the large overlapping polaron tunneling model. By using the Arrhenius equation, the activation energy has been calculated which is nearly equal to the energy required for the hoping of the electron. Both impedance and conductivity analysis demonstrate that the sample exhibits negative temperature coefficient of resistance (NTCR) properties indicating the semiconducting type of material at high temperatures. The anti-ferromagnetic character of the material is observed from the nature of magnetic hysteresis loop. The leakage current analysis suggests that the conduction process in the material follows the space charge limited conduction phenomenon. Such material will be helpful for modern electronic devices and spintronic applications.     

Synthesis of Light Hydrocarbons from Biogas and Hydrogen: Investigation of a Fe-Mn-K/MgO Catalyst

Fischer-Tropsch synthesis of the CO₂ in biogas aims at producing light hydrocarbons and increasing its calorific value for feeding into the grid. Fe catalysts with Mn and K as promoters are supposed to yield high amounts of light hydrocarbons. Using a Fe-Mn-K/MgO catalyst, a parameter screening and long-term experiments were carried out. The catalyst shows, within the examined range, the highest selectivity to C₂–C₄ hydrocarbons at 450 °C, 8 bar(a), and a gas hourly space velocity of 350 h⁻¹. Calcination of the catalyst resulted in a significant drop of activity and an almost complete loss of selectivity to hydrocarbons. Admixture of steam to the reactant gas lowers the tendency to carbon deposition but also promotes the water-gas shift reaction and results in lower yields of hydrocarbons.     

Development of low-alcohol isotonic beer by interrupted fermentation

Alcohol-free beer with isotonic properties is getting more popular and its production can be carried out by different production strategies; however, interrupted fermentation is still a challenge. Therefore, the objective of this study was to develop a low-alcohol isotonic beer (<0.5% v/v) by interrupted fermentation. Moreover, the major objective is to compare the developed product to commercial beverages (sports drinks, ‘Pilsen' regular beer, alcohol-free beers and low-alcohol isotonic beer). The beverages were evaluated based on pH, alcohol content (% v/v), total titratable acidity (mEq L⁻¹), osmolality (mOsmol kg⁻¹), bitterness International Bitterness Units, colour European Brewery Convention, total phenolic compounds (mg L⁻¹ gallic acid), reducing and total sugars (%) and Na and K contents (mg L⁻¹). The developed low-alcohol isotonic beer presented characteristics similar to sports drinks, with the advantage of being richer in phenolic compounds and suitable osmolality. Despite salts were added in its formulation, the grades attributed to all beers employed in the sensory evaluation, as well as the purchase intention did not present significant differences.     

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd–Bullough–Mikhailov and Tzitzéica–Dodd–Bullough equations, are obtained using the exp_a function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.     

Development of a new ion-imprinted polymer (IIP) with Cd2+ ions based on divinylbenzene copolymers containing amidoxime groups

Polymer Bulletin - In this study, we synthesized a new ion-imprinted polymer (IIP) based on introduction of amidoxime groups in acrylonitrile, complexation with Cd2+ ions and polymerization with...     

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.