Keynote Speaker
Prof. Dr. Silke Christiansen
Scale-bridging, multi-modal characterization of energy materials and devices - batteries as an example
S.H. Christiansen1,2,3
1Fraunhofer Institute for Ceramic Technology and Systems - IKTS, Äußere Nürnberger Str. 62, 91301 Forchheim, Germany
2Free University of Berlin, Physics department, Arnimallee 14, 14195 Berlin, Germany
3Institute of Nanotechnology and Correlative Micorscopy - INAM, Äußere Nürnberger Str. 62, 91301 Forchheim, Germany
The escalating complexity in energy material and device design, along with the heterogeneity of emerging materials, demands enhancements in device performance that necessitate scale-bridging optimizations, from macro to nano. This holds for solarcells, fuelcells, batteries and others.
When we take a look at batteries, the development of novel battery materials and devices is increasingly focusing on enhancing reliability, sustainability, and longevity to meet higher standards of quality and promote a circular economy. Key to optimizing these devices is the precise identification and categorization of nanoscale imperfections in materials, alongside a comprehensive understanding of material and device interactions across different scales.
In this context, the integration of various analytical methods becomes critical for success. Techniques such as electron, ion, optical, and x-ray microscopy, along with complementary spectroscopies (optical, mass), are essential. The atomic force microscopy (AFM) technique, essentially with ist conductive AFM modes complements the analytical context. The employment of nanoGPS technology is instrumental in achieving a cohesive correlation among these diverse analytical and imaging modalities.
Our presentation will showcase the application of AFM techniques in battery research and demponstrate the nanoGPS [1] technology to establish connections across multimodal analytical and imaging techniques. We will explore both the preparatory and subsequent analytical workflows. Additionally, we will discuss the integration of machine learning strategies to analyze the complex, heterogeneous data derived from these studies, aiming to enhance the efficiency of material and device optimization significantly. This approach not only promises to refine the understanding of material properties down to the nano level but also paves the way for more effective and sustainable battery technologies.
[1] O. Acher et al., Meas. Sci. Technol. 32, 045402 (2021)