Preparation and performance of CuFe2O4 and ZnFe2O4 magnetic nanocrystals

Based on the coprecipitation of FeSO4(NH4)2SO4 with CuCl2 and ZnSO4, CuFe2O4 and ZnFe2O4 nanocrystals were successfully synthesized. The morphology and the crystal structures of the nanoparticles were studied via SEM, TEM and XRD, which showed that MFe2O4 samples were formed aggregated nanoparticles with crystal sizes of 16~20 nm with a narrow dispersion in size. The samples had the typical spinel structures. Magnetic analyses demonstrated that the CuFe2O4 sample had the saturation magnetization (Ms) of 10.10 emu/g with the coercivity of 3459.39 Oe, while the ZnFe2O4 sample had the Ms of 8.27 emu/g with the coercivity of 25.42 Oe at room temperature, respectively.

Structural and magnetic properties analysis of trivalent Al3+ ions substituted Ni-Zn-Co nano-spinel ferrites

This study explored the structural, morphological, optical, and magnetic properties of Ni0.4Zn0.35Co0.25Fe2−x Al x O4 (0 ≤ x ≤ 0.12) nano-spinel ferrites. Nanocrystalline cubic structure formation and weight loss percentage were determined by thermogravimetric analysis and differential scanning calorimetry (TGA - DSC). Single-phase cubic spinel structures with Fd3m space group of synthesized samples were confirmed by Rietveld refinement X-ray diffraction (XRD) data. The particle sizes were found to be in the range of 6.7 nm–5.25 nm, and agglomeration occurs inside the ferrite samples. The atomic planes and strong crystallinity were detected through SAED images. The characteristic peaks of the Raman spectra identified the bonding between the cations and anions in the sub-lattices. The optical bandgaps (E g ) were found to be in the range of 2.1 eV–2.52 eV. S-shape hysteresis (M-H) loops identified the superparamagnetic nature of the nano-samples. The studies’ outcomes indicated the applicability for biomedical applications of these nano samples.

Synthesis and Characterization of Non-Stoichiometric Li1.1Co0.3Fe2.1O4 Ferrite Nanoparticles For Humidity Applications

Humidity sensor plays a crucial role in determining the efficiency of materials and the precision of apparatuses. To measure and control humidity, a non-stoichiometric Li1.1Co0.3Fe2.1O4 mesopores sensor is synthesized by a modified citrate auto combustion technique. The XRD study confirms that prepared nanoparticles are cubic spinel structures having Fd3m space group. The crystallite size is approximate 36 nm. Thermal analyses measurements confirm that the samples become thermally stable starting from 600 °C. Additionally, the kinetic studies of the prepared samples are calculated via a pseudo-first-order kinetic model. The temperature dependence of AC conductivity is found to increase with increasing the temperature. These observations are explained in various models. The resistivity mechanism of humidity sensors is studied via Complex impedance spectroscopy (CIS). Its impedance data is fitting to a corresponding circuit, to achieve a simulation of the sample under study. This fitting is detected by the Nyquist plot (Cole-Cole). The obtained data confirms that the studied samples are very sensitive to humidity and can be commercially used as a humidity sensing element.

A Comparison of Order-Disorder in Several Families of Cubic Oxides

Order-disorder on both cation and oxygen sites is a hallmark of fluorite-derived structures, including pyrochlores. Ordering can occur on long- and short-range scales and can result in persistent metastable states. In various cubic oxide systems, different types of disorder are seen. The purpose of this paper is to review and compare the types and energetics of order-disorder phenomena in several families of cubic oxides having pyrochlore, weberite, defect fluorite, perovskite, rocksalt, and spinel structures. The goal is to better understand how structure, composition, and thermodynamic parameters (enthalpy and entropy) determine the feasibility of different competing ordering processes and structures in these diverse systems.

Crystal-Site-Based Artificial Neural Networks for Material Classification

In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

Oxonitride pigments

Oxonitride pigments belong to the inorganic yellow, orange and red colorants. They were developed to complement the yellow, orange and red color range. Oxonitride compositions that are interesting for pigment uses crystallize in perovskite, chalcolamprite and spinel structures. The most suitable compositions for pigment purposes are most probably LaTaON2 and CaTaO2N. The synthesis of oxonitride pigments is technically difficult and takes place using a solid state-gas reaction. The pigments are characterized by excellent optical and application characteristics in particular regarding brilliance and color strength, strong hiding power, good dispersibility, light fastness and temperature stability.