HyperSpy is an open
source Python library that provides tools
facilitating interactive data analysis of multi-dimensional datasets (e.g. a 2D
arrays of spectra a.k.a spectrum images).
The project makes it easy to map analytical procedures used on individual
signals to multi-dimensional data, as well as facilitating access to the wider
scientific Python ecosystem for advanced analysis. Through this approach,
HyperSpy provides access to analysis pipelines that are often difficult (if not
impossible) to implement within commercial microscopy and data collection
software. While originally developed for scanning microscopy data analysis,
HyperSpy’s modular structure makes it easy to add features to analyze many
different kinds of signals.
At the latest count, HyperSpy has been cited over 200
and has an extremely active developer- and user-base throughout a range of
scientific fields. Development of HyperSpy is open-source and is not directly
funded by any source, meaning all
authors are grateful
for support within their respective research groups to make the data analysis
tools that they have developed available open source. For more details of my
specific contributions, take a look at the commit
The atomic-level structure and chemistry of materials ultimately dictate their observed macroscopic properties and behavior. As such, an intimate understanding of these characteristics allows for better materials engineering and improvements in the resulting devices. In our work, two material systems were investigated using advanced electron and ion microscopy techniques, relating the measured nanoscale traits to overall device performance. First, transmission electron microscopy and electron energy loss spectroscopy (TEM-EELS) were used to analyze interfacial states at the semiconductor/oxide interface in wide bandgap SiC microelectronics. This interface contains defects that significantly diminish SiC device performance, and their fundamental nature remains generally unresolved. The impacts of various microfabrication techniques were explored, examining both current commercial and next-generation processing strategies. In further investigations, machine learning techniques were applied to the EELS data, revealing previously hidden Si, C, and O bonding states at the interface, which help explain the origins of mobility enhancement in SiC devices. Finally, the impacts of SiC bias temperature stressing on the interfacial region were explored. In the second system, focused ion beam/scanning electron microscopy (FIB/SEM) was used to reconstruct 3D models of solid oxide fuel cell (SOFC) cathodes. Since the specific degradation mechanisms of SOFC cathodes are poorly understood, FIB/SEM and TEM were used to analyze and quantify changes in the microstructure during performance degradation. Novel strategies for microstructure calculation from FIB-nanotomography data were developed and applied to LSM-YSZ and LSCF-GDC composite cathodes, aged with environmental contaminants to promote degradation. In LSM-YSZ, migration of both La and Mn cations to the grain boundaries of YSZ was observed using TEM-EELS. Few substantial changes however, were observed in the overall microstructure of the cells, correlating with a lack of performance degradation induced by the H2O. Using similar strategies, a series of LSCF-GDC cathodes were analyzed, aged in H2O, CO2, and Cr-vapor environments. FIB/SEM observation revealed considerable formation of secondary phases within these cathodes, and quantifiable modifications of the microstructure. In particular, Cr-poisoning was observed to cause substantial byproduct formation, which was correlated with drastic reductions in cell performance.