For the past decade, silicon (Si) as a material for negative electrodes of Li-ion batteries has been considered among the most promising candidates for replacing commonly used graphite. However, Si-based electrodes suffer from severe degradation, which depends on the type of Si materials used. Generally, the degradation of Si is mainly viewed in terms of particle fracturing during lithiation accompanied by constant growth of the solid electrolyte interphase (SEI). At the same time, the reversed process, delithiation, has received little attention. The present work demonstrates the morphological changes of the Si components of electrodes occurring during electrochemical cycling through electron microscopy analyses. These changes are rationalized through the migration of Si, resulting in the formation of Si dendrites embedded in SEI. With the assistance of ReaxFF modeling, we demonstrate that the delithiation predominantly drives this process. The present study reveals that fracturing of Si particles is not the only cause for degradation, as the Si surfaces dramatically change after prolonged cycling, resulting in the formation of Si dendrites.

The galvanostatic charge–discharge (GCD) behaviour of silicon (Si) is known to depend strongly on morphology, cycling conditions and electrochemical environment. One common method for analysing GCD curves is through differential capacity, but the data processing required necessarily degrades the results. Here we present a method of extracting empirical information from the delithiation step in GCD data for Si at C-rates above equilibrium conditions. We find that the function is able to quickly and accurately determine the best fit to historical half-cell data on amorphous Si nanowires and thin films, and analysis of the results reveals that the function is capable of distinguishing the capacity contributions from the Li${}_{3.5}$Si and Li${}_2$Si phases to the total capacity. The method can also pick up small differences in the phase behaviour of the different samples, making it a powerful technique for further analysis of Si data from the literature. The method was also used for predicting the size of the reservoir effect (the apparent amount of Li remaining in the electrode), making it a useful technique for quickly determining voltage slippage and related phenomena. This work is presented as a starting point for more in-depth empirical analysis of Si GCD data.

Recent years have witnessed an exponential increase in battery research, driven by the need to develop efficient and sustainable energy storage systems. One of the main tools in battery research is battery cycling experiments, providing insights into the performance, lifetime and quality of the battery. Due to the large variety of battery testing equipment and the resulting multitude of different and often proprietary data formats, combined with the large number of parameters involved, managing and processing battery testing data has often been a difficult and tedious task. The Python library cellpy assists in solving these problems by 1. providing tools to read different data formats, 2. converting those into one common data format that also includes relevant battery-specific metadata, and 3. providing a data structure equipped with a set of methods that helps the user to easily perform simple and in-depth analyses of both single data sets and collections of data sets.