Artikel in referierten Fachzeitschriften

  • T. Mitsch, Y. Krämer, J. Feinauer, G. Gaiselmann, H. Markötter, I. Manke, A. Hintennach and V. Schmidt, Preparation and characterization of Li-ion graphite anodes using synchrotron tomography. (pdf). Materials 7 (2014), 4455-4472.
  • Y. Krämer, C. Birkenmaier, J. Feinauer, A. Hintennach, C.L. Bender, M. Meiler, V. Schmidt, R.E. Dinnebier and T. Schleid. A new method for quantitative marking of deposited Lithium via chemical treatment on graphite anodes in Lithium-ion cells. (pdf). Chemistry - A European Journal 21 (2015), 6062–6065.
  • J. Feinauer, T. Brereton, A. Spettl, M. Weber, I. Manke and V. Schmidt, Stochastic 3D modeling of the microstructure of lithium-ion battery anodes via Gaussian random fields on the sphere. (pdf). Computational Materials Science 109 (2015), 137-146.
  • J. Feinauer, A. Spettl, I. Manke, S. Strege, A. Kwade, A. Pott and V. Schmidt, Structural characterization of particle systems using spherical harmonics. (pdf). Materials Characterization 106 (2015), 123-133.
  • L. Pfaffmann, C. Birkenmaier, M. Müller, W. Bauer, T. Mitsch, J. Feinauer, Y. Krämer, F. Scheiba, A. Hintennach, T. Schleid, V. Schmidt and H. Ehrenberg, Investigation of the electrochemical active surface area and lithium diffusion in graphite anodes by a novel OsO4 staining method. (pdf). Journal of Power Sources 307 (2016), 762-771.

Preprints

  • S. Hein, J. Feinauer, D. Westhoff, I. Manke, V. Schmidt and A. Latz, Stochastic microstructure modelling and electrochemical simulation of lithium-ion cell anodes in 3D. (pdf). Preprint (submitted)
  • P. Pietsch, D. Westhoff, J. Feinauer, J. Eller, F. Marone, M. Stampanoni, V. Schmidt and V. Wood, Quantifying microstructural dynamics and electrochemical activity of graphite(-silicon) lithium ion battery anodes. Preprint (submitted)

Working Papers

  • J. Feinauer, D. Westhoff, S. Hein, S. Schmidt, J. Zausch, S. Rave, M. Ohlberger, A. Latz and V. Schmidt, MULTIBAT: Unified workflow for fast electrochemical 3D simulations of lithium-ion cells combining virtual stochastic microstructures, electrochemical degradation models and model order reduction. Working paper (under preparation)
  • J. Feinauer, D. Westhoff, L. Petrich, D. Finegan, P. Shearing and V. Schmidt, Crack detection in lithium-ion battery cells using machine learning. Working paper (under preparation)
  • D. Westhoff, J. Feinauer, K. Kuchler, T. Mitsch, I. Manke, S. Hein, A. Latz and V. Schmidt, Parametric stochastic 3D model for the microstructure of anodes in lithium-ion power cells. Working paper (under preparation)