Column solid phase extraction and determination of ultra-trace Au,Pd and Pt in environmental and geological samples

A new sorbent based on cysteine modified silica gel (SiG-cys) was prepared and studied for preconcentration and separation of noble metals Au(III), Pd(II), Pt(II), Pt(IV). Its extraction efficiency was examined by batch and column solid phase extraction procedures. Laboratory experiments performed showed that sorbent is characterized with high selectivity, permiting quantitative sorption (93–97%) of noble metals Au, Pd and Pt from acidic media 0.1–2 mol L^? 1 HCl and unsignificant sorption (less than 2%) for common base metals like Cu, Fe, Mn and Zn. The analytes retained on the sorbent are effectively eluted with 0.1 mol L^? 1 thiourea in 0.1 mol L^? 1 HCl and measured by ETAAS or ICP OES under optimal instrumental parameters. The sorbent showed high mechanical and chemical stability and extraction efficiency was not changed after 500 cycles of sorption/desorption. The sorbent was successfully applied in analyticals procedures for preconcentration and determination of Au, Pd and Pt in geological and soil samples. Detection limits (3σ criteria) achieved, depending on the instrumental methods used are: ETAAS (0.005 μg L^? 1 for Au in river and sea water, 0.002 μg g^? 1 for Au in copper ore and copper concentrate); ICP OES (0.03 μg L^? 1 for Pd and 0.06 μg L^? 1 for Pt in river and sea water, 0.006 μg g^? 1 for Pd in copper ore and copper concentrate and 0.002 μg g^? 1 for soluble Pt in soil). The accuracy of the procedures developed was confirmed by added/found method for sea and river water; by the analysis of national certified materials (copper ore and copper concentrate for Au and Pd) and by determination of the sum of soluble Pt(II) + Pt(IV) in spiked soil samples.     

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ?₁ norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Levels of <Superscript>232</Superscript>Th activity in the South Adriatic Sea marine environment of Montenegro

²³²Th activities in the South Adriatic Sea-water, surface sediment, mud with detritus, seagrass (Posidonia oceanica) samples, and the mullet (Mugilidae) species Mugil cephalus, as well as soil and sand from the Montenegrin Coast, were measured using the six-crystal spectrometer PRIPYAT-2M, which has relatively high detection efficiency and a good sensitivity, and allows a short acquisition time, and measuring samples of any shape, without preliminary preparation and calibration measurements for different sample geometries. An average ²³²Th activity concentration in surface soil layer is found to be 40.33 Bq kg⁻¹, while in sand—4.7 Bq kg⁻¹. The absorbed dose rate in air due to ²³²Th gamma radiation from surface soil layer ranged from 11.76 to 63.39 nGy h⁻¹, with a mean of 24.06 nGy h⁻¹. Corresponding average annual effective dose rate has been found to be 0.03 mSv y⁻¹. The absorbed dose rates due to the thorium gamma radiation in air at 1 m above sand surface on the Montenegrin beaches have been found to be from 0.41 to 9.08 nGy h⁻¹, while annual effective dose rates ranged from 0.0005 to 0.011 mSv y⁻¹. ²³²Th activity concentration in seawater ranged from 0.06 to 0.22 Bq L⁻¹, as in the mullet (Mugil cephalus) whole individuals from 0.63 to 1.67 Bq kg⁻¹. Annual intake of ²³²Th by human consumers of this fish species has been estimated to provide an effective dose of about 0.003 mSv y⁻¹.     

Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra

Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents a method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with a sparse probabilistic model, that is, sources are constrained to be sparse in support and amplitude. This model is validated on experimental pure component mass spectra. Under a sparse prior, a nonlinear problem is converted into an equivalent linear one comprised of original sources and their higher‐order, mostly second‐order, monomials. The influence of these monomials, which stand for error terms, is reduced by preprocessing a matrix of mixtures by means of robust principal component analysis and hard, soft and trimmed thresholding. Preprocessed data matrices are mapped in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions by means of an empirical kernel map. Sparseness‐constrained nonnegative matrix factorizations in RKHS yield sets of separated components. They are assigned to pure components from the library using a maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of eight dependent components from three nonlinear mixtures and to extraction of 25 dependent analytes from nine nonlinear mixture mass spectra recorded in nonlinear chemical reaction of peptide synthesis. Copyright © 2014 John Wiley & Sons, Ltd.     

Nonlinear mixture‐wise expansion approach to underdetermined blind separation of nonnegative dependent sources

Underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents a method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture‐wise mapping of observed data in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness‐constrained nonnegative matrix factorization (NMF) therein. Thus, the original problem is converted into new one with increased number of mixtures, increased number of dependent sources, and higher‐order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, which is the case for mass spectra of analytes, sparseness‐constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on the original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of 10 dependent components from five mixtures and to extraction of 10 dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Impulsive control for a class of neural networks with bounded and unbounded delays

In this paper, we study the problem of global exponential stability for a class of impulsive neural networks with bounded and unbounded delays and fixed moments of impulsive effect. We establish stability criteria by employing Lyapunov functions and Razumikhin technique. An illustrative example is given to demonstrate the effectiveness of the obtained results.     

Rates of dediazoniations of arenediazonium ions complexed with 18-, 21- and 24-membered crown ethers

The evaluation of the kinetics of dediazoniation of benzenediazonium tetrafluoroborate and $\text{p}$-chlorobenzenediazonium tetrafluoroborate in 1,2-dichloroethane at 50°C in the presence of 18-crown-6, 21-crown-7 and dicyclohexano-24-crown-8 demonstrates that the rate constant for the dediazoniation within the complex is smallest, and the equilibrium constant for complex formation is largest for the complex with 21-crown-7.     

Center of Gravity (COG) Method as a Tool in Processing of Voltammetric Signals

The center of gravity method (COG) was applied (for the first time) in voltammetry (polarography) as a tool for very precise determination of peak potentials, and signal shifts. Basically, the adjustment of the method consists of finding the optimal fraction of the peak that contains information about peak position, either of original signal or of its 1^st or 2^nd derivatives, along with optimal selection of parameters for Savitzky? Golay smoothing of original curves and elimination of baseline influence. The principle of the method and its validation were demonstrated and checked on simulated differential pulse polarograms (DPP) representing a series of curves for the determination of stability constants of labile metal complexes (DeFord‐Hume method). It was shown that COG outperforms the classical “one‐point” method (OPM) in precision and accuracy, providing excellent results even if a very large step potential (e.g. 10 mV) was used. The problems of reduced accuracy and precision in case of curved (non‐ideal) baseline and asymmetric peaks were successfully overcome by applying COG on curves transformed by 1^st and/or 2^nd derivatives. The method was additionally examined in details on a simple experimental dataset of cadmium chloride complexes in 4 mol dm^?3 ionic strength solution and on a more complex dataset of uranyl selenate complexes in 3 mol dm^?3 ionic strength solution. Stability constants obtained by COG agree well with those in the literature with much better precision than the classical one‐point method (checked by standard error of the fit).