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EN
In the present study, calix[4]arene and its derivate 25,27-di(4-nitrobenzyl)-26,28-dihydroxycalix[4]arene were irradiated for 72 hours by ⁶⁰Co gamma rays at dose rate of 0.720 kGy/h. These irradiated samples were analyzed by EPR method and it was found that one type of radical was produced in calix[4]arene, and two type of radicals were produced in 25,27-di(4-nitrobenzyl)-26,28-dihydroxycalix[4]arene. The measurements were performed in the temperature range of 273-400 K using x-band EPR spectrometer. It was observed that the spectra, obtained from two samples, have very weak temperature dependence. Hyperfine and g parameters were calculated for two samples.
EN
The electron magnetic resonance data on high-spin (S =2) manganese(III) 3d⁴ ion in tetraphenylporphyrinato chloride complex (MnTPPCl) obtained by high-frequency techniques are reanalysed. Preliminary results of semiempirical modeling of the spin Hamiltonian parameters for Mn(III) in MnTPPCl are presented. The microscopic spin Hamiltonian approach is utilized to predict the zero-field splitting and the Zeeman electronic parameters. It is found that for Mn(III) ions in MnTPPCl matching the experimental spin Hamiltonian parameters and the theoretical ones based on the ligand-field energy levels (Δ_{i}) within the ⁵D multiplet only may not be suitable for this system. Contributions due to the levels arising from the higher-lying ³H multiplet need to be taken into account in order to determine the reasonable values of microscopic parameters describing Mn(III) ions in MnTPPCl.
EN
Spectroscopic and magnetic properties of Fe²⁺ (3d⁶; S=2) ions at orthorhombic sites in FeX₂·4H₂O (X = F, Cl, Br, I) crystals are compared with those in [Fe(H₂O)₆](NH₄)₂(SO₄)₂ (FASH). The microscopic spin Hamiltonian modeling utilizing the package MSH/VBA enables prediction of the zero-field splitting parameters and the Zeeman electronic ones. Wide ranges of values of the microscopic parameters, i.e. the spin-orbit (λ), spin-spin (ρ) coupling constants, and the crystal-field (ligand-field) energy levels (Δp_{i}) within the ⁵D multiplet are considered to establish the dependence of the zero-field splitting parameters b_{k}^{q} (in the Stevens notation) and the Zeeman factors g_{i} on λ, ρ, and Δp_{i}. By matching the theoretical spin Hamiltonian parameters and the experimental ones measured by EMR, the suitable values of λ, ρ, and Δp_{i} are determined. The novel aspect is prediction of the fourth-rank zero-field splitting parameters and the ρ (spin-spin)-related contributions, not considered in previous studies. The MSH predictions provide guidance for high-magnetic field and high-frequency EMR measurements.
EN
Oxidative stress and the excess of free radicals accelerate the ageing process of human skin. The application of skin cream with antioxidant compounds could reduce the damage caused by free radicals. In this work we studied two types of skin creams with extracts from aronia (Aronia melanocarpa), elderberry (Sambucus nigra) and bilberry (Vaccinium myrtillus) because of their high content of anthocyanins, i.e. strong natural antioxidants. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability of the skin creams with berry extracts were studied with ESR spectroscopy. The artificial neural networks were applied to optimize the berry extract concentration and storage time for oil-in-water and water-in-oil creams. Based on experimental results chokeberry and elderberry extracts in oil-in-water cream base revealed higher DPPH radical scavenging ability than in the corresponding water-in-oil. Artificial neural networks predicts maxima of DPPH radical scavenging for 1-week stored elderberry (2.23 mg DPPH/g) and 1-week stored chokeberry (5.84 mg DPPH/g) and bilberry (5.26 mg DPPH/g) 0.76% extracts in oil-in-water creams. The maxima of DPPH radical scavenging for water-in-oil creams were predicted for 6-week stored 0.8% aronia extract, freshly prepared 0.76% bilberry extract and 1-week stored 0.56% elderberry extract. The artificial neural networks predicted values are in good agreement with the experimental values. DPPH-EPR could be combined with artificial neural networks to optimize the extract concentration, and the type of cream base as well as to predict the effect of storage based on a limited number of experiments and samples.
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