Synthesis of 3D cubic of Eu3+/Cu2O with clover-like faces nanostructures and their application as an electrochemical sensor for determination of antiretroviral drug nevirapine

In this experiment, an original three-dimensional (3D) cubic of europium (Eu) ³⁺/cuprous oxide (Cu₂O) with clover-like face-centered nanostructures (Eu³⁺/Cu₂O CLFNs) was successfully synthesized to determine nevirapine (C₁₅H₁₄N₄O), using electrochemical methods. The surface morphology of the Ns was correspondingly identified through different techniques, including energy dispersive X-ray spectroscopy (EDX), X-ray powder diffraction (XRD), and scanning electron microscopy (SEM). In this sense, the synergistic influence of the Eu³⁺/Cu₂O CLFNs enhanced the electrocatalytic capability of the electrode via a modified glassy carbon (GC) and raised the active site. Employing various approaches, the modified electrode was then analyzed through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Moreover, kinetic parameters and electrochemical ones were estimated by means of voltammetric methods. For the determination of nevirapine applying the Eu³⁺/Cu₂O CLFNs/GC electrode (GCE), differential pulse voltammetry (DPV) was further utilized. Under optimized conditions, the range of linear responses was between 0.01 and 750.0 μM with the limit of detection (LOD) of 3.6 nM, whereas the recommended sensor sensitivity was equal to 0.1244 μA/μM. The response time of approximately 3.5 min and the reproducibility (inter- and intra-electrode reproducibility of 2.26% and 1.51%, respectively) were subsequently achieved. It was concluded that sensors have been thus far exploited to determine nevirapine in real samples with favorable outcomes, indicating that electrocatalysis modifier can be assumed as one of appropriate catalysts.     

Compilation of directly measured nuclear spins of ground states and long-lived isomers

A compilation of the nuclear spins of ground and isomeric states measured by direct methods is presented. The first compilation of direct measurements of nuclear spins and moments was published in 1976 (G. H. Fuller, J. Phys. Chem. Ref. Data 5, 835, (1976)) with literature covered up to 1974. To our knowledge, the present work is the first such compilation since then. It is anticipated that the area of direct spin measurement will continue to expand using the state-of-the-art radioactive ion-beam and laser techniques. Literature cutoff date for the present compilation is February 2013. It is intended that the present compilation will be kept updated in a timely manner.     

Fabrication and characterization of exfoliated chitosan–gelatin–montmorillonite nanocomposite nanofibers

Polymeric nanofibers as one of the most known nanotechnology products have huge potential applications in many fields due to their high aspect ratio and porosity, being capable of formation of three-dimensional structures and having great mechanical and biological properties. Chitosan is a natural abundant polymer which has attracted huge interests in biomedical and biological industries due to its biocompatible, biodegradable, and non-toxicity properties. However, electrospinning of chitosan is found to be a great challenge, blending it with other polymers such as gelatin was explored as means to improve the morphological deficiencies of chitosan nanofibers and facilitate its electrospinnability. On the other hand, montmorillonite (MMT) has been attracted great attention due to its remarkable improvement in properties of polymeric composites nanofibers. The main objective of this work was on effect of concretion of gelatin–chitosan blends and MMT on morphology of resulted nanocomposite nanofibers. The x-ray diffraction data demonstrated the exfoliation of MMT layers. The morphology of electrospun chiosan–gelatin–MMT composite nanofibers was characterized using scanning electron microscope (SEM). The miscibility of blend was determined using SEM and Fourier transform infrared spectrometer/attenuated total reflectance.     

Metal and Metal Oxide Electrocatalysts for Redox Flow Batteries

Redox flow batteries (RFBs) are one of the promising technologies for large‐scale energy storage applications. For practical implementation of RFBs, it is of great interest to improve their efficiency and reduce their cost. One of the key components of RFBs that can greatly influence the efficiency and final cost is the electrode. The chemical and structural nature of electrodes can modify the kinetics of redox reactions and the accessibility of the electroactive species to available active sites. The ideal electrocatalyst for RFBs must have good activity for the desirable redox reaction, provide a high surface area, and exhibit sufficient conductivity and durability over repeated use. One strategy is to coat the electrode with metal and metal oxide electrocatalysts. Metal electrocatalysts have the advantage of high conductivity, while metal oxide catalysts are usually less expensive and so more economically attractive. In order to gain a better understanding of the performance of the electrocatalysts in RFBs, a comprehensive review of the progress in the development of metal and metal oxide electrocatalysts for RFBs is provided and a critical comparison of the latest developments is presented. Finally, practical recommendations for advancement of electrocatalysts and effective transfer of knowledge in this field are provided.     

Estimation of soil dispersivity using soft computing approaches

The accurate estimation of soil dispersivity (α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant (L), mean grain size (D ₅₀), soil bulk density (ρ _b), and contaminant velocity (V _c). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination (R ²)], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R ² = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R ² = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution (L) and bulk density (ρ _b) have, respectively, the most and the least effect on the soil dispersivity.