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Electrode Optimization
Identifying inactive material

Charge Photometry captures how individual particles store charge. By analysing the charge photometry contrast for all particles during battery operation, it is possible to rapidly quantify the proportion of active and inactive material within the electrode (and more).

High-performance, long-term battery operation ideally requires all particles in the electrode to experience the same state-of-charge (SoC) during cycling. However, as charging rates increase, electrode-level charge heterogeneity between active particles can become prominent, limiting rate capability and causing degradation in the active materials.

Charge photometry can help identify such charge heterogeneity by providing direct visualization of the electrochemical activity of individual active particles during battery operation across a large field of view.  

Charge Photometry on a Ni-rich NMC cathode in a half cell.

Figure 1. Charge Photometry on a Ni-rich NMC cathode in a half cell. (left) Optical images at open circuit voltage before charging (OCV, top) and at the end of charge (4.5 V, bottom). (right) Applied cell current and cell voltage response, compared to the charge photometry contrast averaged of the entire image. The cell was charged at C/5, held at 4.5 V for 30 min, followed by a 30 min OCV, C/5 discharge and OCV. Scale bar – 10 µm.

Figure 1 (left) depicts optical images of a single-crystal Ni-rich nickel-manganese-cobalt oxide electrode (NMC, 95.5% material loading). The top image is taken at OCV before cycling the cell, while the bottom image corresponds to the top of charge (4.5 V). The bright objects in both images represent NMC particles at the surface of the electrode and intensity changes between the images indicate that delithiation in the particles has taken place.

The charge photometry response averaged across the imaged section of the electrode is shown alongside the cycling protocol in Figure 1 (right, green). As the electrode is charged (delithiated), the charge photometry contrast increases by ~12% in an almost linear fashion. During the voltage hold and OCV period at the top of charge, the contrast remains approximately constant, before the discharge step (lithiation) reduces the contrast to its initial value.

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Figure 2. Particle activity quantification. (top) Charge photometry activity image of a Ni-rich NMC cathode after charging to 4.5 V. Orange – active particles, blue – inactive particles. The material performance scope is shown as an inset. Scale bar – 10 µm. (bottom) Example charge photometry contrast curves of active and inactive particles, highlighting their difference and scope for further analysis.

To evaluate the quality of the electrode, we identified hundreds of individual particles in the imaged electrode and evaluated the particle-specific charge photometry contrast at the top of charge (4.5 V). As displayed in Figure 2 (top), particles that showed a substantial contrast increase during delithiation are classified as active (orange), while particles with no or marginal contrast change are classified as inactive (blue).


For the electrode examined here, 582 particles were detected across the full field of view and 63 particles were inactive during a C/5 charge. This corresponds to 11% of the inactive material. No trend in size or distribution could be identified for the inactive particles, suggesting connectivity issues as the main cause of particle inactivity.

Example charge photometry contrast traces during the charge-discharge cycle for an example active (orange) and inactive (blue) particle are shown below. Notably, the contrast curve of the active particle deviates substantially from the electrode-averaged curve shown in Figure 1, suggesting that this particle did not (dis)charge at a constant rate. Analysis of this behaviour enables more detailed classification of active particles based on their individual (de)lithiation rates and provides valuable insights for electrode optimization.

More about this and further examples on how charge photometry is used for electrode optimization can be found in our  

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