TRAINING COURSE: Correlative Materials Characterization

Research and development in materials characterization techniques are increasingly needed for modern materials science, for innovation in high-tech branches and to guarantee the functionality, performance and reliability of advanced products. In this context, Correlative Materials Characterization using various techniques has the potential for a significant impact in the field of materials science and engineering.

With this novel approach, correlative microscopy will be expanded by including spectroscopic techniques and diffraction. In this advanced training course, we will consider the whole chain from equipment hardware through data acquisition strategies up to advanced data analysis including the application of AI algorithms. We will demonstrate how the scientific community will be able to benefit from access to rapidly expanding data sets generated by (correlative) materials characterization, and how the use of machine learning algorithms in image analysis (2D and 3D) and data analysis (spectra, diffraction patterns, etc.) enable materials scientists to uncover new types of structures in large amounts of data in an efficient way.

Topics and contents


Correlative materials characterization of hierarchical materials and thin films

Welcome and introduction:
Multi-scale and correlative materials characterization

Prof. Dr. Ehrenfried Zschech, Dresden Fraunhofer Cluster Nanoanalysis (Germany)

Hierarchical materials
Prof. Dr. Wilhelm Schwieger, Friedrich Alexander Universität Erlangen-Nürnberg (Germany)

Hyperspectral and multispectral imaging
Dr. Wulf Grählert, Fraunhofer Institute for Material and Beam Technology Dresden (Germany)

Incl. Breaks from 10:30-10:45 and 11:45-12:00


Correlative microscopy with or in electron microscopes

SEM, FIB and TEM for 3D imaging and correlative studies
Prof. Dr. Eva Olsson, Chalmers University of Technology Gothenburg (Sweden)

Correlative light and electron microscopy to study the dynamics and ultrastructure of biological samples
Prof. Dr. Thomas Müller-Reichert, Technische Universität Dresden (Germany)

12:00 Correlative probe and electron microscopy using AFM-in-SEM
Dr. Jan Neuman, NenoVision Brno (Czech Republic)

Incl. Breaks from 10:30-10:45 and 11:45-12:00


X-ray techniques and correlative materials characterization

Multi-scale X-ray computed tomography
Prof. Dr. Ehrenfried Zschech, Dresden Fraunhofer Cluster Nanoanalysis (Germany)

Spectromicroscopy at the synchrotron
Prof. Dr. Gerd Schneider, Helmholtz Zentrum Berlin (Germany)

Advanced data analysis

Machine learning algorithms for the analysis of spectroscopy data
Dr. Janis Timoshenko, Fritz Haber Institut of Max Planck Gesellschaft Berlin (Germany)

Summary and final remarks
Prof. Dr. Ehrenfried Zschech, Dresden Fraunhofer Cluster Nanoanalysis (Germany)

Incl. Breaks from 10:00-10:15 and 11:15-11:30

Your benefit 


  •  The course will provide knowledge needed for correlative materials characterization:
     - Hierarchical materials and multi-scale characterization
     - Hyperspectral and multispectral imaging of thin films
     - CLEM: Correlative light electron microscopy
     - Multi-scale X-ray computed tomography and spectromicroscopy
     - Advanced concepts for data analysis including Artificial Intelligence algorithms.
  • The course will cover data acquisition, data prosessing and data analysis, including the application of machine learning algorithms, of advanced materials for specific use-cases in basic research and industrial application.
  • New results from the combined utilization of analytical techniques will be presented, and application-specific solutions in the fields of renewable energies, biomimetics and microelectronics will be provided.
  • The potential of the use of correlative materials characterization to describe structure, composition and chemical binding as well as 3D morphology and microstructure of materials will be explained by an experienced team of lecturers from academia and industry with knowledge in the fields of materials science, physical and chemical materials analysis as well as advanced data analysis. 

 Target audience 


  • The course is intended for individuals who wish to expand their knowledge in the field of multi-scale, multi-spectral and correlative materials characterization, both in basic research and in practical applications for materials development. The subjects covered in this course extend from materials science and materials analysis up to the current challenges in industry.
  • Scientists, engineers and technicians working in industry – particularly in quality control and R&D – as well as scientists, engineers and particularly PhD students from research institutes and universities, who are interested to extend their knowledge in materials characterization, will benefit from this course.


Organisation | Information

Lecture Hours in detail: Day 1 to 3: 09:00 - 13:00, 26 - 28 April 2021

Dial-in Information will be send by e-mail to the participants one day before the training course starts. 

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