VIT Bhopal University

A Sn-doped Eu 2 O 3 /α-MnO 2 nanocomposite was synthesized via a one-pot chemical co-precipitation method and evaluated as a noble-metal-free electrocatalyst for the oxygen evolution reaction (OER) in alkaline media. X-ray diffraction (XRD) confirmed the formation of tetragonal α-MnO 2 with well-dispersed cubic Eu 2 O 3 nanodomains. At the same time, the absence of distinct Sn-based secondary phases suggested effective lattice incorporation or high dispersion of Sn species. Halder–Wagner analysis revealed a reduction in crystallite size and changes in microstrain following Eu₂O₃ incorporation and Sn modification. High-resolution transmission electron microscopy (HRTEM) demonstrated well-defined lattice fringes with clear interplanar spacings, confirming high crystallinity and heterointerface formation. In contrast, selected-area electron diffraction patterns revealed the polycrystalline nature of the nanocomposite. X-ray photoelectron spectroscopy (XPS) confirmed stable Eu³⁺ and Sn⁴⁺ oxidation states, mixed Mn 3 + /Mn 4+ redox chemistry, and an enhanced concentration of oxygen vacancies. Electrochemical investigations showed that the Sn–Eu 2 O 3 /α-MnO 2 electrode exhibits improved charge-transfer characteristics and moderate enhancement of OER activity, delivering a low overpotential of 350 mV at 10 mA cm −2 , a reduced Tafel slope, improved charge-transfer characteristics, and increased interfacial capacitive behaviour. The enhanced electrocatalytic performance is attributed to the synergistic effects of Sn-induced defect engineering, Eu 2 O 3 -mediated interfacial charge modulation, and optimized Mn redox chemistry, highlighting the potential of this nanocomposite for efficient alkaline OER applications. This study shows that using both rare-earth oxide promotion and heteroatom doping can change the electronic structure, defect chemistry, and interfacial charge transfer in MnO 2 -based catalysts. This combined approach offers a promising way to develop affordable and scalable noble-metal-free electrocatalysts for sustainable water-splitting technologies.

A Sn-doped Eu2O3 /α-MnO2 nanocomposite was synthesized via a one-pot chemical co-precipitation method and evaluated as a noble-metal-free electrocatalyst for the oxygen evolution reaction (OER) in alkaline media. X-ray diffraction (XRD) confirmed the formation of tetragonal α-MnO2 with well-dispersed cubic Eu2O3 nanodomains. At the same time, the absence of distinct Sn-based secondary phases suggested effective lattice incorporation or high dispersion of Sn species. Halder–Wagner analysis revealed a reduction in crystallite size and changes in microstrain following Eu₂O₃ incorporation and Sn modification. High-resolution transmission electron microscopy (HRTEM) demonstrated well-defined lattice fringes with clear interplanar spacings, confirming high crystallinity and heterointerface formation. In contrast, selected-area electron diffraction patterns revealed the polycrystalline nature of the nanocomposite. X-ray photoelectron spectroscopy (XPS) confirmed stable Eu³⁺ and Sn⁴⁺ oxidation states, mixed Mn3 +/Mn4+ redox chemistry, and an enhanced concentration of oxygen vacancies. Electrochemical investigations showed that the Sn–Eu2O3/α-MnO2 electrode exhibits improved charge-transfer characteristics and moderate enhancement of OER activity, delivering a low overpotential of 350 mV at 10 mA cm−2, a reduced Tafel slope, improved charge-transfer characteristics, and increased interfacial capacitive behaviour. The enhanced electrocatalytic performance is attributed to the synergistic effects of Sn-induced defect engineering, Eu2O3-mediated interfacial charge modulation, and optimized Mn redox chemistry, highlighting the potential of this nanocomposite for efficient alkaline OER applications. This study shows that using both rare-earth oxide promotion and heteroatom doping can change the electronic structure, defect chemistry, and interfacial charge transfer in MnO2-based catalysts. This combined approach offers a promising way to develop affordable and scalable noble-metal-free electrocatalysts for sustainable water-splitting technologies.

A novel series of nematic liquid crystals with rod-like structures, derived from [1,1'-biphenyl]-4-yl(E)-4-((4-n-alkoxybenzylidene) amino) benzoate (XnI) and 4 -benzoylphenyl (E)-4-((4-(n-alkoxy) benzylidene) amino) benzoate (XnII) have been successfully synthesised, where n ranges from 4, 6, 8 & 10. Proton magnetic resonance, mass and infrared spectroscopy were used to analysed and characterised. Differential scanning calorimetry (DSC) and polarising optical microscopy (POM) were used to examine the liquid crystal behaviour of all produced substances. All homologous series XnI and XnII compounds displayed a nematic mesophase. The impact of molecular flexibility, the range of transition temperatures and the comparative research were all systematically investigated. The X4I-X10I homologues exhibit an enantiotropic nematic mesophase within the temperature range of 177-148 degrees C, while the X4II-X10II homologues show a nematic mesophase at temperatures between 169 and 122 degrees C. XnI & XnII compounds studied using Gauss View 6 software and were subjected to geometry optimisation using the Density Functional Theory (DFT) calculations at the B3LYP/6 -31+G(d,p) level of theory as implemented in Gaussian 16 quantum chemistry package. The band gap values, calculated by the difference of the energy values of HOMO and LUMO, were higher for the XnI series as compared to the XnII compounds.

As attacks on the network environment are rapidly becoming more sophisticated and intelligent in recent years, the limitations of the existing signature-based intrusion detection system are becoming more evident. For new attacks such as Advanced Persistent Threats (APT), the signature pattern has a problem of poor generalisation performance. Research on intrusion detection systems based on machine learning is being actively conducted to solve this problem. However, in the actual network environment, the attack sample is collected less than the normal sample, so it suffers a class imbalance problem. When a supervised learning-based anomaly detection model is trained with these data, the results are biased toward normal samples. In this paper, AutoEncoder (AE) is used to perform single-class anomaly detection to solve this imbalance problem. The experimental evaluation was conducted using the CIC-IDS2017 dataset, and the performance of the proposed method was compared with supervised models to evaluate the performance.

Let eta be a prime ideal of an arbitrary ring zeta's. We study the quotient ring zeta/eta without assuming the primeness of zeta involving prime ideals under the action of multiplicative b-generalized derivations F : zeta -> Q(mr) satisfying central algebraic identities. We also generalize Herstein's result [I. N. Herstein, A note on derivations, Canad. Math. Bull. 21 1978, 3, 369-370, Theorem 2] in our Theorem 2.7.