Homogeneous liquid-liquid extraction of uranium(VI) using tri-n-octylphosphine oxide.

A simple and efficient method for the selective separation and preconcentration of uranium(VI) using homogeneous liquid-liquid extraction was developed. Tri-n-octylphosphine oxide (TOPO) and tri-n-butylphosphate (TBP) were investigated as complexing ligands, and perfluorooctanoate ion (PFOA-) was applied as a phase separator agent under strongly acidic conditions. Under the optimal conditions ([PFOA-] = 1.7 x 10(-3) M, [TOPO] = 5.4 x 10(-4) M, [HNO3] = 0.3 M, [acetone] = 3.2% v/v) 10 microg of uranium in 40 ml aqueous phase could be extracted quantitatively into 8 microl of the sedimented phase. The maximum concentration factor was 5000-fold. However, an effort for the quantitative extraction using TBP was inefficient and the percent recovery was at most 56.7. The influence of the type and concentration of acid solution, optimum amount of the ligand, type and volume of the organic solvent, concentration of PFOA, volume of the aqueous sample and effect of different diverse ions on the extraction and determination of uranium(VI) were investigated. The proposed method was applied to the extraction and determination of uranium(VI) in natural water samples.     

Kinetic determination of silver ion by its perturbation on Belousov-Zhabotinskii oscillating chemical reaction using potentiometric method.

Silver ion can perturb the Belousov-Zhabotinskii (B-Z) oscillating chemical reaction. Therefore, the B-Z oscillating system was applied in the determination of silver ion by using a platinum wire as an indicator electrode in the potentiometric method. The amplitude of the potentiometric oscillation increased linearly in proportion to [Ag+] in the range of 9.42 x 10(-6) M to 2.54 x 10(-4) M, with a correlation coefficient of 0.999 under the optimum conditions. The obtained LOD (2sigma) was 8.85 x 10(-6) M and the relative standard deviation (RSD) for five measurements of 1 x 10(-4) M silver ion was 5%. The influence of some potentially interference was also investigated.     

Simultaneous kinetic-spectrophotometric determination of hydrazine and acetylhydrazine in micellar media using the H-point standard addition method.

The H-point standard addition method (HPSAM), based on a spectrophotometric measurement for the simultaneous determination of hydrazine and acetylhydrazine, is described. This method is based on the difference between the rates of their reactions with N,N-dimethylaminobenzaldehyde (DAB) in the presence of sodium dodecyl sulfate (SDS) in acidic media. The results showed that hydrazine and acetylhydrazine could be determined simultaneously in the range of 0.020 - 0.70 and 0.20 - 5.0 mg L(-1), respectively. Under the working conditions, the proposed method was successfully applied to the simultaneous determination of hydrazine and acetylhydrazine in several synthetic mixtures and plasma and water samples.     

Highly selective PVC-membrane electrodes based on Co(II)-Salen for determination of nitrite ion.

A cobalt(II) derivative was used as a suitable ionophore for the preparation of a polymeric membrane nitrite-selective electrode. The electrode reveals a Nemstian behavior over a very wide NO2- ion concentration range (1.0 x 10(-6)-1.0 x 10(-1) M) and a very low detection limit (5.0 x 10(-7) M). The potentiometric response is independent of the pH of solution in the pH range 4.0-9.5. The electrode shows advantages such as low resistance, fast response and, most importantly, good selectivity relative to a wide variety of inorganic and organic anions. In fact, the selectivity behavior of the proposed NO2- ion-selective electrode shows great improvements compared to the previously reported electrodes for nitrite ion. The proposed electrodes could be used for at least 2 months without any significant changes in potentials. The electrode was successfully applied to the determination of nitrate ion concentrations in sausage and milk samples.     

A heterocyclic peptide nanotube

An open-ended hollow tubular structure is designed based on hydrogen-bond-directed self-assembly of a chimeric cyclic peptide subunit comprised of alternating alpha- and epsilon-amino acids. The design features a novel 1,4-disubstituted-1,2,3-triazole epsilon-amino acid and its utility as a peptide backbone substitute. The N-Fmoc-protected epsilon-amino acid was synthesized in high yield and optical purity in three steps from readily available starting materials and was employed in solid-phase peptide synthesis to afford the desired cyclic peptide structure. The cyclic peptide self-assembly has been studied in solution by (1)H NMR and mass spectrometry and the resulting tubular ensemble characterized in the solid state by X-ray crystallography.     

A novel organocatalytic multi-component reaction: an efficient synthesis of polysubstituted pyrano-fused spirooxindoles

A practical, simple, and efficient method for the synthesis of pyrano-fused spirooxindoles via an organocatalytic three-component reaction of isatins, malononitrile, and dialkyl acetylenedicarboxylate in the presence of 3,4-dimethylaniline as an organocatalyst in ethanol is reported. The structures of these products are confirmed by IR, ¹H NMR and ¹³C NMR spectroscopy, mass spectrometry, and single-crystal X-ray diffraction studies.     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

A quantile-based Time at Risk: A new approach for assessing risk in financial markets

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.