Structure of Manganese Oxide Nanoparticles Extracted via Pair Distribution Functions

Influence of SiO2 Embedding on the Structure, Morphology, Thermal, and Magnetic Properties of Co0.4Zn0.4Ni0.2Fe2O4 Particles

(Co_0.4Zn_0.4Ni_0.2Fe₂O₄)_α(SiO₂)_(100-α) samples obtained by embedding Co_0.4Zn_0.4Ni_0.2Fe₂O₄ nanoparticles in SiO₂ in various proportions were synthesized by sol-gel process and characterized using thermal analysis, Fourier-transform infrared spectroscopy, X-ray diffraction, transmission electron microscopy, inductively coupled plasma optical emission spectrometry, and magnetic measurements. Poorly crystalline Co-Zn-Ni ferrite at low annealing temperatures (500 °C) and highly crystalline Co-Zn-Ni ferrite together with traces of crystalline Fe₂SiO₄ (800 °C) and SiO₂ (tridymite and cristobalite) (1200 °C) were obtained. At 1200 °C, large spherical particles with size increasing with the ferrite content (36-120 nm) were obtained. Specific surface area increased with the SiO₂ content and decreased with the annealing temperature above 500 °C. Magnetic properties were enhanced with the increase in ferrite content and annealing temperature.

Characterisation of intergrowth in metal oxide materials using structure-mining: the case of γ-MnO2

Manganese dioxide compounds are widely used in electrochemical applications e.g. as electrode materials or photocatalysts. One of the most used polymorphs is γ-MnO₂ which is a disordered intergrowth of pyrolusite β-MnO₂ and ramsdellite R-MnO₂. The presence of intergrowth defects alters the material properties, however, they are difficult to characterise using standard X-ray diffraction due to anisotropic broadening of Bragg reflections. We here propose a characterisation method for intergrown structures by modelling of X-ray diffraction patterns and pair distribution functions (PDF) using γ-MnO₂ as an example. Firstly, we present a fast peak-fitting analysis approach, where features in experimental diffraction patterns and PDFs are matched to simulated patterns from intergrowth structures, allowing quick characterisation of defect densities. Secondly, we present a structure-mining-based analysis using simulated γ-MnO₂ superstructures which are compared to our experimental data to extract trends on defect densities with synthesis conditions. We applied the methodology to a series of γ-MnO₂ samples synthesised by a hydrothermal route. Our results show that with synthesis time, the intergrowth structure reorders from a R-like to a β-like structure, with the β-MnO₂ fraction ranging from ca. 27 to 82% in the samples investigated here. Further analysis of the structure-mining results using machine learning can enable extraction of more nanostructural information such as the distribution and size of intergrown domains in the structure. Using this analysis, we observe segregation of R- and β-MnO₂ domains in the manganese oxide nanoparticles. While R-MnO₂ domains keep a constant size of ca. 1–2 nm, the β-MnO₂ domains grow with synthesis time.

A methodology for characterisation of γ-MnO₂ intergrowths has been developed. By combining supercell modelling, structure-mining and machine learning, both qualitative and quantitative information on intergrowth domain distributions are extracted.

Atomic structure of an FeCrMoCBY metallic glass revealed by high energy x-ray diffraction

Amorphous bulk metallic glasses with the composition Fe₄₈Cr₁₅Mo₁₄C₁₅B₆Y₂have been of interest due to their special mechanical and electronic properties, including corrosion resistance, high yield-strength, large elasticity, catalytic performance, and soft ferromagnetism. Here, we apply a reverse Monte Carlo technique to unravel the atomic structure of these glasses. The pair-distribution functions for various atomic pairs are computed based on the high-energy x-ray diffraction data we have taken from an amorphous sample. Monte Carlo cycles are used to move the atomic positions until the model reproduces the experimental pair-distribution function. The resulting fitted model is consistent with ourab initiosimulations of the metallic glass. Our study contributes to the understanding of functional properties of Fe-based bulk metallic glasses driven by disorder effects.

Study of Rechargeable Batteries Using Advanced Spectroscopic and Computational Techniques

Improving the efficiency and longevity of energy storage systems based on Li- and Na-ion rechargeable batteries presents a major challenge. The main problems are essentially capacity loss and limited cyclability. These effects are due to a hierarchy of factors spanning various length and time scales, interconnected in a complex manner. As a consequence, and in spite of several decades of research, a proper understanding of the ageing process has remained somewhat elusive. In recent years, however, combinations of advanced spectroscopy techniques and first-principles simulations have been applied with success to tackle this problem. In this Special Issue, we are pleased to present a selection of articles that, by precisely applying these methods, unravel key aspects of the reduction–oxidation reaction and intercalation processes. Furthermore, the approaches presented provide improvements to standard diagnostic and characterisation techniques, enabling the detection of possible Li-ion flow bottlenecks causing the degradation of capacity and cyclability.

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

We discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co₂₀Fe₆₁B₁₉specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe₂₃B₆as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

Magnetic properties of γ-Fe2O3 nanoparticles in a porous SiO2 shell for drug delivery

A method is presented for synthesizing core-shell nanoparticles with a magnetic core and a porous shell suitable for drug delivery and other medical applications. The core contains multiple γ-Fe2O3 nanoparticles (∼15 nm) enclosed in a SiO2 (∼100-200 nm) matrix using either methyl (denoted TMOS-γ-Fe2O3) or ethyl (TEOS-γ-Fe2O3) template groups. Low-temperature Mössbauer spectroscopy showed that the magnetic nanoparticles have the maghemite structure, γ-Fe2O3, with all the vacancies in the octahedral sites. Saturation magnetization measurements revealed that the density of γ-Fe2O3 was greater in the TMOS-γ-Fe2O3 nanoparticles than TEOS-γ-Fe2O3 nanoparticles, presumably because of the smaller methyl group. Magnetization measurements showed that the blocking temperature is around room temperature for the TMOS-γ-Fe2O3 and around 250 K for the TEOS-γ-Fe2O3. Three dimensional topography analysis shows clearly that the magnetic nanoparticles are not only at the surface but have penetrated deep in the silica to form the core-shell structure.

Structural properties of PbTe quantum dots revealed by high-energy x-ray diffraction

High-energy x-ray diffraction (HE-XRD) experiments combined with an analysis based on atomic-pair-distribution functions can be an effective tool for probing low-dimensional materials. Here, we show how such an analysis can be used to gain insight into structural properties of PbTe nanoparticles (NPs). We interpret our HE-XRD data using an orthorhombic Pnma phase of PbTe, which is an orthorhombic distortion of the rocksalt phase. Although local crystal geometry can vary substantially with particle size at scales below 10 nm, and for very small NPs the particle size itself influences x-ray diffraction patterns, our study shows that HE-XRD can provide a unique nano-characterization tool for unraveling structural properties of nanoscale systems.