Characterization Methods along the Process Chain of Electrical Steel Sheet—From Best Practices to Advanced Characterization

Non-oriented (NO) electrical steel sheets find their application in rotating electrical machines, ranging from generators for wind turbines to motors for the transportation sector and small motors for kitchen appliances. With the current trend of moving away from fossil fuel-based energy conversion towards an electricity-based one, these machines become more and more important and, as a consequence, the leverage effect in saving energy by improving efficiency is huge. It is already well established that different applications of an electrical machine have individual requirements for the properties of the NO electrical steel sheets, which in turn result from the microstructures and textures thereof. However, designing and producing tailor-made NO electrical steel sheet is still challenging, because the complex interdependence between processing steps, the different phenomena taking place and the resulting material properties are still not sufficiently understood. This work shows how established, as well as advanced and newly developed characterization methods, can be used to unfold these intricate connections. In this context, the respective characterization methods are explained and applied to NO electrical steel as well as to the typical processing steps. In addition, several experimental results are reviewed to show the strengths of the different methods, as well as their (dis)advantages, typical applications and obtainable data.

Thermal Effect on Formation Stability Due to Heterogeneity

This study evaluates physical and chemical changes induced by high thermal gradients on the formation and their impact to the stability. The heat sources that effect the formation’s stability are varied, including drilling (due to drilling bit friction), perforation, electromagnetic heating (laser or microwave), and thermal recovery or stimulation (steam, resistive heating, combustion, microwave, etc.). This study uses an integrated approach to characterize rock heterogeneity and mapping heat propagation from different heat sources. The information obtained from the study is vital to accurately design and enhance well completion and stimulation This is an integrated analysis approach combining different advanced characterization and visualization techniques to map heat propagation in the formation. Advanced statistical analysis is also used to determine the key parameters and build fundamental prediction algorithms. Characterization on the samples was performed before, during, and after the exposure to thermal sources; it comprised thin-section, high speed infrared thermography (IR), differential thermal analysis and thermogravimetric analyzer (DTA/TGA), scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), uniaxial stress, and autoscan (provide hardness, composition, velocity, and spectral absorption). The results are integrated, and machine learning is used to derive a predictive algorithm of heat propagation and mapping in the formation with reference to the key formation variables and heterogeneity distribution. Rock heterogeneity affects the rate and patterns of heat propagation into the formation. Within the rock sample, minerals, laminations, and cementations lead to a heterogeneous, and sometimes anisotropic, distribution of thermal properties (thermal conductivity, heat capacity, diffusivity, etc.). These properties are also affected by the rock structure (porosity, micro-cracks, and fractures) and saturation distribution. The results showed the impact of heat on the mechanical properties of the rocks are due to clays dehydration, mineral dissociations, and micro cracks. High speed thermal imaging provides a unique visualization of heat propagation in heterogeneous rocks. Statistical analysis identified key parameters and their impact on thermal propagation; the output was used to build a machine learning algorithm to predict heat distributions in core samples and near-wellbore. Characterizing rock properties and understanding how heterogeneity modifies heat propagation in rocks enables the design of optimal completion and stimulation strategies. This paper discusses how advanced characterization and analysis, combined with novel algorithms, can improve this understanding, and unleash innovation and optimization. The data and information gathered are critical to develop numerical models for field-scale applications.

Advancing the representation of reservoir hydropower in energy systems modelling: The case of Zambesi River Basin

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant’s installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.

Microwave-Assisted Sol–Gel Preparation of the Nanostructured Magnetic System for Solid-Phase Synthesis

This work describes a new synthesis method for core–shell magnetite nanoparticles with a secondary silica shell, functionalized with a linker system (Fe3O4-PABA-SiO2-linker) using a microwave-assisted heating technique. The functionalized solid nanomaterial was used for the nanophase synthesis of peptides (Fmoc route) as a solid support. The co-precipitation method was selected to obtain magnetite nanoparticles and sol–gel technique for silica coating using a microwave-assisted (MW) procedure. The magnetic properties of the nanoparticle core offer the advantage of a quick and easy alternative for the magnetic separation of the product from the reaction mixture, facilitating all the intermediary washing and separation operations. The intermediate and final materials were analyzed by advanced characterization methods. The effectiveness of the nanophase peptide synthesis using this nanostructured material as solid support was demonstrated for a short peptide sequence.

Atomic scale chemical ordering in franckeite—a natural van der Waals superlattice

The mineral franckeite is a naturally occurring van der Waals superlattice which has recently attracted attention for future applications in optoelectronics, biosensors and beyond. Furthermore, its stacking of incommensurately modulated 2D layers, the pseudo tetragonal Q-layer and the pseudo hexagonal H-layer, is an experimentally accessible prototype for the development of synthetic van der Waals materials and of advanced characterization methods to reveal new insights in their structure and chemistry at the atomic scale that is crucial for deep understanding of its properties. While some experimental studies have been undertaken in the past, much is still unknown on the correlation between local atomic structure and chemical composition within the layers. Here we present an investigation of the atomic structure of franckeite using state-of-the-art high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) and atom probe tomography (APT). With atomic-number image contrast in HAADF STEM direct information about both the geometric structure and its chemistry is provided. By imaging samples under different zone axes within the van der Waals plane, we propose refinements to the structure of the Q-layer and H-layer, including several chemical ordering effects that are expected to impact electronic structure calculations. Additionally, we observe and characterize stacking faults which are possible sources of differences between experimentally determined properties and calculations. Furthermore, we demonstrate advantages and discuss current limitations and perspectives of combining TEM and APT for the atomic scale characterization of incommensurately modulated von der Waals materials.