Synthesis of ceria nanoparticles for the catalytic activity of cyclohexene epoxidation and selective detection of nitrite

Based on a few noteworthy features, cerium oxide nanoparticles have gained significance in nanotechnology. The effective microwave combustion method (MCM) and the conventional sol–gel (CRSGM) technologies are used in this study to successfully generate the crystalline CeO₂ nanoparticles (NPs). Additionally, using a variety of spectroscopic and analytical methods, the synthesized CeO₂ NPs are examined to assess to understand their structure and morphology. The XRD patterns of CeO₂ NPs show that the structure exhibits a face-centered cubic lattice. Then, with demonstrated good conversion and selectivity, the impact of the epoxidation reaction of cyclohexene was examined. Finally, it can be said that using CeO₂ nanoparticles is an efficient strategy to increase the catalytic activity toward the epoxidation reaction of cyclohexene. In the presence of acetonitrile as a solvent and H₂O₂ as an oxidant, the catalyst samples utilized in the cyclohexene epoxidation reaction were examined. In this study, the CeO₂ catalyst outperformed all other catalysts in terms of cyclohexene maximal conversion and selectivity. After six prolonged cycles, the conversion of cyclohexene oxidation using CeO₂ NPs shows reasonable recyclability and conversion efficiency, making it the best catalyst for an industrial production application.Additionally, the upgraded CeO₂ nanoparticle electrode for nitrite detection has a linear concentration range (0.02–1200 M), a low detection limit (0.22 M), and a higher sensitivity (1.735 A M⁻¹ cm⁻²). CeO₂ NPs, on the other hand, have a quick response time, excellent sensitivity, and high selectivity. Additionally, the manufactured electrode is used to find nitrite in various water samples. Finally, it can be said that using CeO₂ NPs is an efficient strategy to increase the catalytic activity toward cyclohexene oxidation and nitrite.     

Metalloallostery and Transition Metal Signaling: Bioinorganic Copper Chemistry Beyond Active Sites

Transition metal chemistry is essential to life, where metal binding to DNA, RNA, and proteins underpins all facets of the central dogma of biology. In this context, metals in proteins are typically studied as static active site cofactors. However, the emergence of transition metal signaling, where mobile metal pools can transiently bind to biological targets beyond active sites, is expanding this conventional view of bioinorganic chemistry. This Minireview focuses on the concept of metalloallostery, using copper as a canonical example of how metals can regulate protein function by binding to remote allosteric sites (e.g., exosites). We summarize advances in and prospects for the field, including imaging dynamic transition metal signaling pools, allosteric inhibition or activation of protein targets by metal binding, and metal-dependent signaling pathways that underlie nutrient vulnerabilities in diseases spanning obesity, fatty liver disease, cancer, and neurodegeneration.     

Data Rate Aware Reliable Transmission Mechanism in Wireless Sensor Networks using Bayesian Regularized Neural Network approach

Wireless Sensor Networks consists of interconnected nodes that exchange information wirelessly enabling its deployment in innovative application areas. Network reliability streamline this exchange of information and communication technology to improve the overall performance of the network. The network reliability is categorized as: node reliability and link reliability. In this article, the node reliability is quantified with the help of packet reliability. The packet reliability is enhanced by optimizing the data rate using Bayesian Regularized Neural Network approach, which thus makes the network more reliable and sustainable. The optimization is carried out in three phases: network designing, data rate prediction and reliability evaluation. The network design includes the deployment of sensor nodes for gathering the communication data using NS-2.35. In the next phase, data rate prediction is carried out to enhance the reliability of the network. The reliability of a network is directly influenced by the packet loss ratio. According to research and the network experts, the acceptable threshold limit for the packet loss ratio is 5 percent. The data rate prediction is carried out to minimize the packet loss using the Bayesian Regularized Neural Network algorithm. The packet reliability is measured in terms of packet loss across the wireless network. Finally, a novel framework is presented for evaluating the packet reliability of the wireless network.