Optimized mercapto-modified resorcinol formaldehyde xerogel for adsorption of lead and copper ions from aqueous solutions

Journal of Sol-Gel Science and Technology - Resorcinol formaldehyde (RF) xerogel was modified by mercapto functional groups for removal of Pb(II) and Cu(II) ions from aqueous solutions. The...     

Green and highly efficient approach for the reductive coupling of nitroarenes to azoxyarenes using the new mesoporous Fe3O4@SiO2@Co–Zr–Sb catalyst

Research on Chemical Intermediates - Efficient, green, simple and environmentally friendly approach for the straightforward reductive coupling of nitroarenes to the corresponding azoxyarenes has...     

Ni3S2 Supported on Porous Ball‐milled Silicon,a Highly Selective Electrochemical Sensor for Glucose Determination

In this work, an electrochemical sensor based on Ni₃S₂ nanoparticles supported on porous ball‐milled silicon was fabricated for measuring glucose. At first, the glassy carbon electrode (GCE) surface was modified by Ni₃S₂ nanoparticles supported on a porous ball‐milled silicon substrate. To characterize the modified electrode, N₂ adsorption‐desorption isotherms and BHJ, transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDX), elemental mapping and X‐ray diffraction (XRD) were used. In the following, the effective parameters on the sensor response such as pH, NaOH concentration, catalyst concentration, applied potential, and rotational speed of the electrode were optimized using cyclic voltammetric (CV) and hydrodynamic amperometric methods. Under the optimal conditions, the calibration curve was plotted using the hydrodynamic amperometric method. Three linear regions were obtained from 0.5–134, 134–1246, and 1246–3546 μM, with a detection limit of 0.2 μM for glucose. Finally, the proposed method was used for measuring glucose levels in human blood serums.     

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.     

Solid Phase Extraction of Lead(II) by Sorption on Grinded Eucalyptus Stem and Determination with Flame Atomic Absorption Spectrometry

A sensitive and simple solid‐phase preconcentration procedure for the determination of trace amount of lead by flame atomic absorption spectrometry (FAAS) is developed. The method is based on the adsorption of Pb²⁺ on the column of fine grinded eucalyptus stem adsorbent, elution of the column by nitric acid and subsequent determination by FAAS. The effect of different variables such as pH, eluent type, flow rate and interfering ions on the recovery of the analyte was investigated and optimum conditions were established. The adsorption of lead onto fine grinded eucalyptus stem can formally be described by a Langmuir equation with a maximum adsorption capacity of 4.49 mg g^?1. A preconcentration factor of 50 was achieved using the optimum conditions. The calibration graph was linear in the range 10–125 ng mL^?1 of lead in the initial solution with r = 0.9982. The limit of detection based on 3S_b criterion was 4.5 ng mL^?1 and the relative standard deviation for eight replicate measurements of 30 and 80 ng mL^?1 of iron was 3.6 and 2.8%, respectively. The method was successfully applied to the determination of lead added to well, tap and wastewater samples.