Synthesis and NMR elucidation of novel amino acid cavitand derivatives

The synthesis of seven novel protected amino acid cavitands is reported. All have four pendant n-undecyl chains and ‘headgroups’ connected by a two-carbon spacer at four positions on the aromatic rings. The amino acids employed are glycine, alanine, phenylalanine, leucine, proline, tryptophan, serine, glutamine and lysine. The structures of the compounds were elucidated using one- and two-dimensional NMR techniques, which verified that all tetra-substituted cavitands have symmetrical C_4v conformation. This is the first example of a complete study for amino acid cavitand derivatives.     

Evaluation of Nanostructured coating layers formed on Ni balls during mechanical alloying of Cu powder

A novel coating method utilizing mechanical alloying was used to deposit a nickel-copper solid solution on the surface of nickel balls in a planetary ball mill. Mechanically milled copper powders were deposited by cold welding on the activated surface of the nickel balls. The impact of the nickel balls causes surface wear, leading to formation of mixed layered particles of copper-nickel and rewelding. In addition, diffusion takes place through the coating layer to form a Ni-Cu solid solution. The hardness of the coating reached a threefold increase (HV_0.01594) in comparison with the substrate hardness. Microstructural characterization of the coating surface performed using an optical microscope, SEM, and EPMA indicates that, with appropriate processing conditions, a thick, fully dense coating can be metallurgically bonded to the nickel balls. XRD results revealed the presence of a solid solution and nanocrystalline structure.     

An environmentally friendly synthesis of 1,4-dihydropyrano[2,3-c]pyrazole derivatives catalyzed by tungstate sulfuric acid

An efficient three-component synthesis of 6-amino-4-aryl-5-cyano-3-metriyl-1-phenyl-1,4-dihydropyrano[2,3-c]pyrazoles via a reaction between 3-methyl-1-phenyl-2-pyrazolin-5-one,aromatic aldehydes and malononitrile using tungstate sulfuric acid as a catalyst was described.Mild conditions,good to excellent yields,easily available catalyst and easy work-up are the key features of this method.     

Effect of magnetic field on property of a non-aqueous solvent upon complex formation between kryptofix 22DD with yttrium (III) cation

To understand the effect of a magnetized solvent upon complexation processes between the metal ions and the ligands, we studied the complexation reaction between Y⁺³ cation with the kryptofix 22DD, in non-magnetized and magnetized methanol solvents at different temperatures using the conductometric method. Addition of kryptofix 22DD to the cation solution causes a continuous increase in the molar conductivities which indicates that the mobility of the complexed cation is higher than the uncomplexed one in both non-magnetized and magnetized methanol solvents. The conductance data show that the stoichiometry of the complex formed between the ligand and Y³⁺ cation is 1:1(M:L). The value of stability constant of (kryptofix 22DD.Y)³⁺ complex was determined from conductometric data using a non-linear least-square program (GENPLOT). The results obtained in this investigation, show that the stability constant of the complex decreases when we use magnetized methanol solvent.     

Enhanced Non-enzymatic amperometric sensing of glucose using Co(OH)<Subscript>2</Subscript> nanorods deposited on a three dimensional graphene network as an electrode material

We report on the synthesis of cobalt dihydroxide [Co(OH)₂] nanorods and their deposition on a 3-dimensional graphene network via chemical bath deposition. The structural characterization reveals deposited Co(OH)₂ to consist of flower-like nanorods on a 3-dimensional graphene foam. The nanocomposite was used for glucose sensing by electrocatalytic oxidation of glucose in 1 M KOH solution. Cyclic voltammetry and amperometric studies revealed a high sensitivity for glucose (3.69 mA mM^?1 cm^?2) and a 16 nM detection limit. The nanocomposite offers a large effective surface (11.4 cm²) and is very selective for glucose over potentially interfering materials such as dopamine, ascorbic acid, lactose, fructose and urea, not the least due to a relatively low working potential of 0.6 V (vs. Ag/AgCl). The high sensitivity, low detection limit and very good selectivity of free-standing nanocomposite electrodes are attributed to the synergistic effect of (a) the good electrocatalytic activity of the NRs, and (b) the large surface area with high conductivity offered by the 3D graphene foam.

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Graphical Abstract Cobalt hydroxide [Co(OH)₂] nanorods were deposited on three dimensional graphene (3DG) by a chemical bath deposition method, and the resulting material was used as an electrode for non-enzymatic and specific sensing of glucose in 1 M KOH solution.

     

Identification of single and double sites of phosphorylation by ECD FT-ICR/MS in peptides related to the phosphorylation site domain of the myristoylated alanine-rich c kinase protein

A series of phosphorylated test peptides was studied by electron capture dissociation Fourier transform ion cyclotron resonance mass spectrometry (ECD FT-ICR MS). The extensive ECD-induced fragmentation made identification of phosphorylation sites for these peptides straightforward. The site(s) of initial phosphorylation of a synthetic peptide with a sequence identical to that of the phosphorylation site domain (PSD) of the myristoylated alanine-rich C kinase (MARCKS) protein was then determined. Despite success in analyzing fragmentation of the smaller test peptides, a unique site on the PSD for the first step of phosphorylation could not be identified because the phosphorylation reaction produced a heterogeneous mixture of products. Some molecules were phosphorylated on the serine closest to the N-terminus, and others on one of the two serines closest to the C-terminus of the peptide. Although no definitive evidence for phosphorylation on either of the remaining two serines in the PSD was found, modification there could not be ruled out by the ECD fragmentation data.     

Crowded Trades,Market Clustering,and Price Instability

Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock, the clustering measure captures the degree of trading overlap among any two investors in that stock, based on a comparison with the expected crowding in a null model where trades are maximally random while still respecting the empirical heterogeneity of both stocks and investors. We investigate the effect of crowded trades on stock price stability and present evidence that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.     

Computer simulations of the effect of atomic structure and coordination on the stabilities and melting behaviour of copper surfaces and nano-particles

We have studied the structures and stabilities of copper nano-particles and the melting properties of copper surfaces using interatomic potential-based molecular dynamics simulations, where the (1 1 1) surface has been shown to be the most stable in terms of surface energy and melting behaviour. Low energy shapes of nano-particles are influenced by the surfaces present and therefore have a higher proportion of (1 1 1) surface. The effect of surface structure on stability becomes less marked as the size of the nano-particle is increased. Melting is observed to occur below the bulk melting temperature in all the surfaces investigated, at increasingly lower temperatures from the (1 1 1), (1 0 0), (1 1 0) down to the (2 1 0) surface, confirming their order of decreasing stability. The melting processes of defective close-packed copper surfaces were also simulated. Steps, kinks, and facets were all shown to accelerate the melting of the surfaces. The melting is shown to initiate at the site of the defect and the results demonstrate that it is the low-coordinated atoms, at the step edge or kink, that are more mobile at lower temperatures. These features facilitate surface melting even further below the melting temperature than was observed for the perfect surfaces. Furthermore, facets of (1 0 0) surface were shown to be unstable even at moderate temperatures on the close-packed surface.     

Static and dynamic magnetic characteristics of BaCo0.5Mn0.5Ti1.0Fe10O19

The effect of Mn²⁺Co²⁺Ti⁴⁺ substitution on microwave absorption has been studied for BaCo_0.5Mn_0.5Ti_1.0Fe₁₀O₁₉ ferrite-acrylic resin composites in frequency range from 12 to 20 GHz. X-ray diffraction (XRD), scanning electron microscope (SEM), vibrating sample magnetometer, AC susceptometer and vector network analyzer were used to analyze the structural, magnetic and microwave absorption properties. The results showed that the magnetoplumbite structures for all samples have been formed. Based on microwave measurement on reflectivity, BaCo_0.5Mn_0.5Ti_1.0Fe₁₀O₁₉ may be a good candidate for electromagnetic compatibility and other practical applications at high frequency.     

A Framework for Detecting System Performance Anomalies Using Tracing Data Analysis

Advances in technology and computing power have led to the emergence of complex and large-scale software architectures in recent years. However, they are prone to performance anomalies due to various reasons, including software bugs, hardware failures, and resource contentions. Performance metrics represent the average load on the system and do not help discover the cause of the problem if abnormal behavior occurs during software execution. Consequently, system experts have to examine a massive amount of low-level tracing data to determine the cause of a performance issue. In this work, we propose an anomaly detection framework that reduces troubleshooting time, besides guiding developers to discover performance problems by highlighting anomalous parts in trace data. Our framework works by collecting streams of system calls during the execution of a process using the Linux Trace Toolkit Next Generation(LTTng), sending them to a machine learning module that reveals anomalous subsequences of system calls based on their execution times and frequency. Extensive experiments on real datasets from two different applications (e.g., MySQL and Chrome), for varying scenarios in terms of available labeled data, demonstrate the effectiveness of our approach to distinguish normal sequences from abnormal ones.