Batteries & Energy Storage


Future generation batteries are being driven by challenging tasks within target industries (e.g., maximum driving distance of an electric vehicle). Through development of new energy storage concepts in terms of materials and cell chemistry, researchers can improve the power/energy density, coulombic efficiency and cycle life of batteries. This performance is dependent on the diffusion of ions, electron transport, structure and chemical dynamics of the electrode/electrolyte materials and can be impacted by electrochemical processes such as cycling. Understanding the structure–property relationships for each component and their synergistic behaviors during these electrochemical processes can help researchers better characterize significant changes in the material structure and elemental distribution. Useful information to elucidate changes in ion relocation, lattice expansion/contraction, phase transition and structure/surface reconstruction during these processes include:

  • Measure material composition and uniformity
  • Determine grain boundary losses and activity
  • Defect analysis
    • Understand what reduces the current you collect
    • Quantify contamination levels
  • Analyze chemical phases within batteries


To adequately characterize and understand materials that affect your product’s energy output, you must first ensure each specimen is of the highest quality to resolve the material interface and properly controlled so you manipulate it, when necessary, under environmental stimuli. Once prepared, several techniques are available to better understand the relationship between microstructure, defects and the optical properties of materials.

Unique insight into the chemical and electronic properties of materials at the microscopic level.
Characterize electrical properties of materials and devices at the microscopic level.
Atomic resolution chemical and compositional analysis.
Family of imaging techniques to enhance, map and quantify elements and chemicals in an image with nanometer resolution.
Systematic method to generate a spatially resolved distribution of EELS data.
Award winning, high resolution imaging tools help you to understand material growth, devices ultrastructure and failures.
Real-time observation of growth processes, chemical reactions and oxidation, irradiation effects, mechanical, magnetic, and ferroelectric properties.
High-performance tools to cut, etch, polish and freeze samples for your unique SEM, TEM or STEM application.
Useful to elucidate elemental or chemical characterization of a sample.
Helps you examine crystallographic orientation or texture of materials.

Visit solar, utilities and environment and semiconductor materials and devices to learn about related applications.

Enabling results

Measure composition uniformity

You can distinguish variations in material composition between grains using cathodoluminescence. In this example, variations are shown as a change in color, with the excess of vacancy point defects at grain boundaries highlighted in blue. Image a) shows the secondary electron image of a Cu0.8In0.2AgSe2 film. Image b) shows the overlaid monochromatic cathodoluminescence images: 1.181 eV emission (blue), 1.121 eV (green), and 1.033 eV (red). Images courtesy of A.R. Aquino Gonzales, Ph.D. Dissertation, University of Illinois.

Determine grain boundaries

See how you can reveal unique grain boundaries through secondary electron (left) and panchromatic cathodoluminescence (right) images.

Quantifying contamination levels

SmartEBIC™ experiment (left) and the theory behind the efficiency analysis (right).

Analyze chemical phases within batteries

The LiFePO4 battery was half reduced and then sliced for analysis. During reduction LiFePO4 is reduced to FePO4. With the sample being too beam sensitive to STEM probe, it was done with EFTEM and revealed the following results.

There is a shift in the L3 line position. With the oxidation state of Fe2+ in LiFePO4 and Fe3+ in FePO4. The reference spectra from the two chemically different particles were extracted and used as references to run the multiple linear least squares (MLLS) fitting routine. Thus, a chemical shift of about 2 eV was observed and mapped out using EELS.