Open Access

High entropy alloys (HEAs) are considered as a new class of alloys containing at least 5 elements with concentrations between 5 and 35 atomic percent. There has been a growing interest in HEAs in the material research field in recent years. Due to their adjustable composition, which enables the modifications of mechanical properties (such as hardness, strength and ductility etc) and their stability at high temperatures, HEAs have been the focus of various studies. Especially the corrosion behavior of HEAs has been a wide research interest. Since the grazing exit X-ray fluorescence (GEXRF) offers a non-destructive way to collect notable information regarding the high temperature oxidation, we consider it as a useful method to investigate how HEAs behave in corrosive environments. The main idea of grazing geometry is to enhance the fluorescence signal of the surface. This enables highly sensitive surface analyses of thin protective film on surface in sub-micrometer scale. Position-sensitive area detectors provide information regarding the signal emitted from the sample as a function of emission angle and thus allow depth-sensitive analysis. Furthermore, the data collected from samples of an incidence energy which lays within a specific energy range provides XANES data to determine oxidation states. Moreover, since GEXRF profiles can also be simulated through physical models (Urbach 1999), they enable us to determine the layer thickness of a given sample in a non-destructive way. In this contribution, we present the preliminary results of a conceptual study regarding layer properties of CrCoNi medium entropy alloy. The successful implementation of such methodological concept will pave the way for the investigation of more complex alloys with multiple layers, which is planned for the later phases of the project.

This research group has been demonstrating the significant advantages of using Nb2O5 coatings for functionalizing titanium, aluminium, and stainless steels. Regarding the biomedical sector and considering Ti-6Al-4V alloy, the reactive sputtering technique improved the cell viability, the osteogenic performance of cells involved in the osseointegration process as well as the ability to delay bacterial proliferation. The characteristics of the Nb2O5 coatings were assessed before by using standard methods, which provide information only a few tens of nanometers depth. Given that the Nb2O5 coating fabricated in this work exhibits a thickness of approximately 300 nm, the GE-XANES technique emerges as the most suitable method for this analysis. Additional information was provided with the aid of nanoindentation load-depth (P-h) curves. GE-XANES results indicated the formation of a homogeneous layer of Nb2O5 coating on the Ti-6Al-4V surfaces. The deposition process improved the surface hardness of the Ti-6Al-4V alloy (4.38 GPa versus 5.62 GPa) considering the 2 mN load.

In this contribution, I present a digital twin-based workflow designed to optimize experimental parameters in X-ray emission spectroscopy (XES), with a focus on reproducibility, data integration, and alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles in materials science and engineering (MSE). The developed pipeline begins with automated retrieval of crystallographic information from the Materials Project database based on a given sample composition. This structural data is then used to simulate the corresponding XES spectra using FDMNES, allowing for accurate prediction of element-specific emission lines. The simulated emission lines are fed into an X-ray tracing (XRT) module, which builds a virtual replica of the experimental setup. This digital twin environment enables predictive modeling of spectrometer performance based on geometric configurations. The optimization focuses on two key parameters: the choice of the analyzing crystal and the distance between the sample and crystal (which also defines the crystal–detector distance due to Bragg condition constraints). An active learning algorithm is employed to iteratively adjust these parameters in order to achieve a desired energy-per-pixel (E/pixel) resolution with minimal intensity loss, enabling efficient, data-driven experimental planning. By integrating data-driven simulations with real-time optimization strategies, this workflow supports efficient experiment planning while minimizing resource consumption and human error. Furthermore, all stages of the process—from data collection and simulation to optimization and visualization—are structured to ensure traceability and interoperability, facilitating future reuse and collaborative research. I hope this contribution aligns well with the topic “Workflows for FAIR MSE Data” and offers a concrete example of how digital twins can be harnessed to improve the design, execution, and documentation of spectroscopy experiments in the MSE domain.

Layered materials are fundamental to technological advancements, offering distinct properties that differentiate them from bulk materials. In electronics, for instance, thin-film transistors (TFTs) are used to enhance charge transport and flexibility, thereby improving device performance. In the same way, thin-film photovoltaic devices used in renewable energy use strategic layering to absorb light more efficiently and separate electron-hole pairs more effectively, which leads to higher energy conversion efficiency. In recent decades, the development of new alloys has highlighted the importance of layered materials in another context. Compositionally complex alloys, for example, form multiple oxide layers on their surfaces when they oxidize. Studying these corrosion layers is crucial for understanding material-environment interactions. Typical surface analysis techniques, including X-ray photoelectron spectroscopy (XPS), secondary ion mass spectrometry (SIMS), and Meitner-Auger electron spectroscopy (MAES), provide valuable insights but are constrained by their requirements for high Vacuum conditions and their limited depth analysis. In contrast, X-ray absorption near-edge structure (XANES) spectroscopy presents a versatile and advantageous alternative. It operates effectively under ambient conditions and allows time-resolved measurements, enhancing the analysis of materials in real-time as they undergo structural and compositional changes. This adaptability broadens the scope for material analysis, allowing for a more comprehensive understanding of dynamic processes. Grazing Emission X-ray Fluorescence (GEXRF) spectroscopy stands out as a nondestructive, depth-resolved, element-specific characterization technique important for collecting depth-resolved information at the nanometer scale. Its ability to collect in-Depth resolved information based on the grazing emission angle of the fluorescence Radiation makes it ideal for investigating thin films, corrosion layers, and interfaces within layered materials. The integration of XANES in emission mode with GEXRF enables detailed exploration of the chemical states of the analyzed atom and provides depth-resolved information. This study discusses grazing emission X-ray absorption near-edge structure spectroscopy (GEXANES), a novel layer analysis technique that is created by integrating these two methods. This study also innovatively combines machine learning with GEXANES spectroscopy to reduce experimental times. By using active learning, a subset of machine learning, it refines the data acquisition process, enabling more efficient and streamlined methods. The application of active learning in this context illustrates the potential of data-driven approaches to transform experimental methodologies, particularly in resource-limited environments such as synchrotron facilities, thereby accelerating scientific research and discovery.

Unlike conventional alloys, high entropy alloys are characterized by one or more solid solution phase(s) without a clearly defined solvent, all element contribute to the matrix in a way that is still not entirely understood. In addition, it is not known to what extent classic thermodynamic rules can be applied to these multi-element alloys, especially concerning the question about what factor incites the matrix to undergo a phase transformation. This work tackles directly some of these aspects on a chosen alloy, Al8Cr17Co17Cu8Fe17Ni33 (at.%), which presents a high temperature single-phase γ state and a two-phase state with γ′ precipitates, above and below 900 ◦C, respectively. A combined investigation via microstructural observations, hardness testing, X-ray absorption and photoelectron spectroscopy was carried out above the γ′ formation temperature. Hardness values are independent of the annealing temperatures, microstructural analysis shows no phase formation and X-ray absorption spectroscopy does not reveal observable changes in neither local atomic nor electronic structure, indicating that approaching γ′ formation temperature is not influenced by atomic or electronic rearrangements. Interestingly, short-range chemical order remains quantitatively compatible at any annealing temperature in the single-phase γ state, and the observed preferred pairs Al–Cu and Al–Ni in the γ state match with the γ’ precipitates composition below 900 ◦C.