Adsorption of acidic, basic, and neutral proteins from aqueous samples using Fe3O4 magnetic nanoparticles modified with an ionic liquid

We have prepared and characterized Fe₃O₄ nanoparticles and their binary mixtures (IL-Fe₃O₄) with 1-hexyl-3-methylimidazolium bromide as ionic liquid for use in the adsorption of lysozyme (LYS), bovine serum albumin (BSA), and myoglobin (MYO). The optimum operational conditions for the adsorption of proteins (at 0.05–2.0 mg?mL^?1) were 4.0 mg?mL^?1 of nanoparticles and a contact time of 10 min. The maximum adsorption capacities are 455, 182 and 143 mg for LYS, BSA, and MYO per gram of adsorbent, respectively. The Langmuir model better fits the adsorption isotherms, with adsorption constants of 0.003, 0.015 and 0.008 L?mg^?1, in order, for LYS, BSA, MYO. The applicability of two kinetic models including pseudo-first order and pseudo-second order model was estimated on the basis of comparative analysis of the corresponding rate parameters, equilibrium adsorption capacity and correlation coefficients. The adsorption processes are endothermic. The proteins can be desorbed from the nanoparticles by using NaCl solution at pH 9.5, and the nanoparticles thus can be recycled.

Simultaneous determination of aniline and cyclohexylamine by principal component artificial neural networks.

A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. An artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures.     

Spectrophotometric study of complex formation equilibria in the presence of interference using hard-soft net analyte signal method: application to drug-metal complexation

In this article, the ability of a new and efficient hard–soft method, previously proposed by our research group, is reported for modeling of the complex formation equilibria in the presence of interferences. This method is based on the net analyte signal (NAS) concept, which is a part of total signal that is directly related to the concentration of the component of interest. It monitors the concentration changes of any chemical species involved in the evolutionary process without requiring any pure spectra or having previous knowledge about the presence of the interferences. The proposed hard–soft method based on net analyte signal (HS-NAS) only needs a chemical model for one of the species involved in the reaction under study. The reliability of the method was examined by applying it to the measured data and spectrum of the known real systems of Fe²⁺–azithromycin and Ca²⁺–tetracycline.     

Electrochemical sensing of <Emphasis Type="SmallCaps">D</Emphasis>-penicillamine on modified glassy carbon electrode by using a nanocomposite of gold nanoparticles and reduced graphene oxide

A new electrochemical sensor was developed for determination of D-penicillamine using glassy carbon electrode which had been modified by gold nanoparticles–reduced graphene oxide nanocomposite (AuNPs/RGO/GCE) in aqueous solution. Cyclic voltammetry, transmission electron microscopy and electrochemical impedance spectroscopy were used for characterization of the modified electrode. The results indicated that the kinetic of oxidation reaction of D-penicillamine at the surface of the electrode was controlled by both diffusion and adsorption processes. In 0.1 mol L^?1 phosphate buffer (pH 2.0), the oxidation current increased linearly with concentration of D-penicillamine with a linear range of 5.0 × 10^?6 to 1.1 × 10^?4 mol L^?1 and regression coefficient of R ² = 0.9972. Theoretical detection limit, defined based on 3σ of the blank signal (n = 9) divided by the slope of the linear regression equation, was 3.9 × 10^?6 mol L^?1 D-penicillamine using differential pulse voltammetry. The developed method was successfully applied to the determination of D-penicillamine in pharmaceutical formulation and blood serum samples.     

Extraction and high performance liquid chromatographic determination of 3-indole butyric acid in pea plants by using imidazolium-based ionic liquids as extractant

In this paper, imidazolium-based ionic liquids [C₄mim][PF₆], [C₆mim][PF₆], [C₈mim][PF₆], [C₆mim][BF₄] and [C₈mim][BF₄] were tested as extracting solvents for removal of 3-indole butyric acid (IBA) from aqueous media with subsequent determination using HPLC. Percent extraction of IBA was strongly affected by pH of aqueous phases and the chemical structures of ionic liquids (ILs). Extraction of IBA was quantitative in the pH values lower than pK_a of IBA. Considering both extraction and stripping efficiencies of IBA, [C₄mim][PF₆] was found to act more efficient than other studied ILs. Capacity of [C₄mim][PF₆] was 17.6 × 10⁻⁴ mmol IBA per 1.0 mL of IL. Ionic strength of aqueous phase and temperature had shown no serious effects on extraction efficiency of IBA. A preconcentration factor of 100 and a relative standard deviation of 1.16% were obtained. It was found that ionic liquid phase was reusable almost five times for extraction/stripping purposes. 3-Indole acetic acid showed interferential effect in the extraction step. In order to assess the applicability of the method, extraction and stripping of IBA from pea plants and some other samples were studied.     

Characterization of a Novel Organic Solvent Tolerant Protease from a Moderately Halophilic Bacterium and Its Behavior in Ionic Liquids

An extracellular protease was purified from a novel moderately halophilic bacterium Salinivibrio sp. strain MS-7 by the combination of an acetone precipitation (40–80 %) step and a DEAE-cellulose anion exchange column chromatography. Kinetic parameters of the enzyme exhibited V _max and K _m of 130 U/mg and 1.14 mg/ml, respectively, using casein as a substrate. The biochemical properties of the enzyme revealed that the 21-kDa protease had a temperature and pH optimum of 50 °C and 8.0, respectively. The enzyme was strongly inhibited by phenylmethylsulfonyl fluoride, Pefabloc SC, chymostatin, and also EDTA, indicating that it belongs to the class of serine metalloproteases. Interestingly, Ba²⁺ and Ca²⁺ (2 mM) strongly enhanced the enzyme activity, while Fe²⁺ and Mg²⁺ activated moderately and Zn²⁺, Ni²⁺, and Hg²⁺ decreased the enzyme activity. The effect of organic solvents with different logP on the purified protease revealed complete stability in toluene, ethyl acetate, chloroform, and n-hexane at 10 and 50 % (v/v) and moderate stability even in 50 % of DMSO and ethanol. The behavior of the MS-7 protease in three imidazolium-based ionic liquids exhibited suitable activity in these green solvent systems, especially in 1-hexyl-3-methylimidazolium hexafluorophosphate ([C₆MIM][PF₆]). Comparison of the purified protease with other previously reported proteases suggests that strain MS-7 secrets a novel organic solvent-tolerant protease with outstanding activity in organic solvents and imidazolium-based ionic liquids, which could be applied in low water synthetic section of industrial biotechnology.     

Efficient Immobilization of Porcine Pancreatic α-Amylase on Amino-Functionalized Magnetite Nanoparticles: Characterization and Stability Evaluation of the Immobilized Enzyme

The potential of the modified magnetic nanoparticles for covalent immobilization of porcine pancreatic α-amylase has been investigated. The synthesis and immobilization processes were simple and fast. The co-precipitation method was used for synthesis of magnetic iron oxide (Fe₃O₄) nanoparticles (NPs) which were subsequently coated with silica through sol–gel reaction. The amino-functionalized NPs were prepared by treating silica-coated NPs with 3-aminopropyltriethoxysilane followed by covalent immobilization of α-amylase by glutaraldehyde. The optimum enzyme concentration and incubation time for immobilization reaction were 150 mg and 4 h, respectively. Upon this immobilization, the α-amylase retained more than 50 % of its initial specific activity. The optimum pH for maximal catalytic activity of the immobilized enzyme was 6.5 at 45 °C. The kinetic studies on the immobilized enzyme and its free counterpart revealed an acceptable change of K_m and V_max. The Km values were found as 4 and 2.5 mM for free and immobilized enzymes, respectively. The V_max values for the free and immobilized enzymes were calculated as 1.75 and 1.03 μmol mg^?1 min^?1, in order, when starch was used as the substrate. A quick separation of immobilized amylase from reaction mixture was achieved when a magnetically active support was applied. In comparison to the free enzyme, the immobilized enzyme was thermally stable and was reusable for 9 cycles while retaining 68 % of its initial activity.     

Unusual Substitution in Hydrazinobenzoic Acid upon Complexation. Crystal and Molecular Structure of Hydrazinobenzoic Acid Hydrochloride

Crystallography Reports - The crystal and molecular structure of 2-hydrazinobenzoic acid was determined. In solution in the presence of copper(II) chloride, 2-hydrazinobenzoic acid is transformed...     

Application of artificial neural networks as a technique for interference removal: kinetic-spectrophotometric determination of trace amounts of Se(IV) in the presence of Te(IV)

Artificial neural networks (ANNs) are among the most popular techniques for nonlinear multivariate calibration in complicated mixtures using spectrophotometric data. In this study we propose a computer-based method for removing Te(IV) interference in the determination of Se(IV) using artificial neural networks. In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The resulting RMSE of prediction for selenium was obtained as 0.108.