Iliad Scanning Transmission Electron Microscope Resources

This comprehensive research, performed by Maria Meledina, Dileep Krishnan, Cigdem Ozsoy-Keskinbora, Hamed Heidari, Xiaochao Wu, Ulrich Simon, Sorin Lazar, Peter Tiemeijer, and Paolo Longo, explores the use of advanced 2D and 3D multimodal spectroscopic techniques for understanding the structure of Cu-doped ceria catalysts at the nanoscale, which are widely relevant in catalysis applications.

This study by Daen Jannis, Nicolas Gauquelin, Maria Meledina, Yuchen Zhao, Yunzhong Chen, and Jo Verbeeck presents a novel method to overcome the challenges of simultaneous ELNES mapping of multiple elements in heterogenic compounds. Using an elegant approach that employs any number of offsets without adding overhead on the dead time of the detector, the team successfully achieved easy correlation of different ELNES and low loss structures.

This study by Sorin Lazar, Maria Meledina, Claudia Schnohr, Thomas Hoeche, Peter Tiemeijer, Paolo Longo, and Bert Freitag presents a quantitative comparison of X-ray absorption spectra with ELNES and EXELFS at energies at 9 keV. The study showcases how EELS can be used to study structure and property relationships and demonstrates the superior performance of a new experimental setup, which allows for high energy resolution and high collection efficiency while minimizing acquisition time.

This research paper by Wouter Van den Broek, Daen Jannis, and Jo Verbeeck presents a new linear background model for electron energy loss spectra. The model, which employs convexity constraints, is shown to provide a better description of both simulated and real measurements than the conventional power-law model, especially for wide energy ranges. This development improves elemental quantification results and offers a fast linear fit with a guaranteed unique solution, reducing the need for user input. 

This study by Jo Verbeeck, Daen Jannis, Wouter Van den Broek, Arno Annys, Zezhong Zhang, and Sandra Van Aert focuses on the development of a completely autonomous data processing workflow for electron energy loss spectroscopy. The authors propose improvements to the physical model and background modelling process and introduce automated feature detection to identify elements in a given dataset. This approach results in a more precise, accurate, and user-friendly method for quantifying EELS spectra. This innovative approach not only enhances the reliability of EELS data processing but also broadens its application scope, making it an essential analytical tool even for non-expert users.