Observation and quantification of nanoscale processes in lithium batteries by operando electrochemical (S)TEM.

An operando electrochemical stage for the transmission electron microscope has been configured to form a "Li battery" that is used to quantify the electrochemical processes that occur at the anode during charge/discharge cycling. Of particular importance for these observations is the identification of an image contrast reversal that originates from solid Li being less dense than the surrounding liquid electrolyte and electrode surface. This contrast allows Li to be identified from Li-containing compounds that make up the solid-electrolyte interphase (SEI) layer. By correlating images showing the sequence of Li electrodeposition and the evolution of the SEI layer with simultaneously acquired and calibrated cyclic voltammograms, electrodeposition, and electrolyte breakdown processes can be quantified directly on the nanoscale. This approach opens up intriguing new possibilities to rapidly visualize and test the electrochemical performance of a wide range of electrode/electrolyte combinations for next generation battery systems.

Applying shot boundary detection for automated crystal growth analysis during in situ transmission electron microscope experiments.

In situ scanning transmission electron microscopy is being developed for numerous applications in the study of nucleation and growth under electrochemical driving forces. For this type of experiment, one of the key parameters is to identify when nucleation initiates. Typically, the process of identifying the moment that crystals begin to form is a manual process requiring the user to perform an observation and respond accordingly (adjust focus, magnification, translate the stage, etc.). However, as the speed of the cameras being used to perform these observations increases, the ability of a user to "catch" the important initial stage of nucleation decreases (there is more information that is available in the first few milliseconds of the process). Here, we show that video shot boundary detection can automatically detect frames where a change in the image occurs. We show that this method can be applied to quickly and accurately identify points of change during crystal growth. This technique allows for automated segmentation of a digital stream for further analysis and the assignment of arbitrary time stamps for the initiation of processes that are independent of the user's ability to observe and react.