Nanostructured Zn-doped cobalt ferrites (Co_1−xZn_xFe₂O₄, where x ranges from 0.0 to 1.0 with a step of 0.1) were studied in order to elucidate the relation between their cationic distribution within the spinel sublattices and catalytic properties. The thermal transformation of the precursors (metal hydroxides) obtained through the hydroxide co-precipitation method was studied by DTA, TGA and FTIR. The thermal behavior of the precursors showed that cobalt ferrite was formed at a lower temperature (376 °C) in comparison to zinc ferrite (478 °C). FTIR analysis revealed vibrational bands around 400 cm⁻¹ and 600 cm⁻¹ related to (MO₄)⁶⁻ and (MO₆)⁹⁻ groups, respectively. The cationic distribution was determined from Mössbauer spectra analysis and the results showed that Zn ions occupy the A-sites, while Co and Fe ions are located in the A- and B-sites. The spectra indicate spinel magnetic ordering in samples with x = 0.0…0.5. The increase in Zn content influences the difference in the Pauling electronegativities, distances between magnetic ions (hopping length) and polaron radius of Co_1−xZn_xFe₂O₄ ferrites, inducing changes in the ionic bond strength for the A- and B-sites. Photoelectrochemical measurements demonstrated that the introduction of Zn²⁺ into the spinel structure modifies the ability of the materials to reduce O₂. Whereas photoinduced reduction of dioxygen at CoFe₂O₄ competes successfully with anodic photocurrent generation (observed as a decrease of photocurrent), for ZnFe₂O₄ this process is negligible. The relation between the cationic distribution (analyzed in terms of antistructure modeling) and the catalytic activity of spinel ferrites was demonstrated for the example of the soot combustion process. It was shown that the octahedral cobalt centers exhibit a higher catalytic activity than the tetrahedral ones.

The electronic spectrum of alizarin (AZ) in methanol solution was measured and used as reference data for color prediction. The visible part of the spectrum was modelled by different DFT functionals within the TD-DFT framework. The results of a broad range of functionals applied for theoretical spectrum prediction were compared against experimental data by a direct color comparison. The tristimulus model of color expressed in terms of CIE XYZ and CIE Lab parameters was applied both to experimental and predicted spectra. It was found that the HSE03 method along with the 6-31G(d,p) basis set provides the most accurate color prediction, much better than other commonly used functionals such as B3LYP, CAM-B3LYP or PBE0. Besides, the influence of potential errors, introduced by theoretical predictions on color estimation, was examined for different wavelengths. The obtained results showed that color prediction is significantly dependent on the type of basis set and functional applied. The proposed methodology provides a simple, straightforward and more reliable way of theoretical protocols validation than just a comparison of experimental and estimated values of maximum absorbance wavelength.

Synthesis of silver nanoparticles (NPs) has been performed using partially hydrolysed star‐shaped graft copolymer of polyacrylamide to dextran as auxiliary capping agent. The star‐branched copolymer proved to be a very good template for NP synthesis due to high local concentration of functional groups. Good biocompatibility of the copolymer was confirmed by phagocytic index value equal to 84%. In addition to this, the green synthesis approach was ensured using glucose as non‐toxic reducing agent. Surface plasmon resonance spectra of the obtained NPs contained two characteristic bands situated at 270–300 and 420–450 nm. Transmission electron microscopy revealed that NPs synthesised in neutral solution (pH = 7) have average diameters equal 10–60 nm with a small portion of NP aggregates. Synthesis carried out at pH = 12 resulted in increased yield of NPs probably due to increased reducing power of glucose in alkaline medium. NPs synthesised in alkaline solution were sized from 20 to 300 nm and contained a large portion of NP aggregates.