The rapid uptake of lithium-ion batteries (LIBs) for large scale electric vehicle and energy storage applications requires a deeper understanding of the degradation mechanisms that contribute to fading performance. Capacity fade has been attributed to a variety of complex and interlinking degradation mechanisms such as phase transitions, electrolyte decomposition and transition metal dissolution. These parasitic reactions are still poorly understood, however many of them evolve gases as a side product. Here we present a novel cell design that enables ultra-sensitive, fully quantified and time resolved detection of volatile species evolving from an operating LIB with on-chip electrochemistry mass spectrometry. The technique’s electrochemical performance and mass transport is described by a finite element model and then experimentally used to demonstrate the variety of new insight into LIB performance it is able to provide. Oxygen is observed to evolve from a LiNiMnCoO2 cathode, and the ensuing electrolyte degradation caused by the oxygen release is monitored. The solid electrolyte interphase reaction on graphite is monitored here too, in a variety of electrolyte systems, enabling the deconvolution of lithium consuming parasitic reactions. Finally, the improved time resolution from the novel technique reveals the first direct evidence of electrocatalytic carbon dioxide reduction to ethylene in a LIB. This finding not only elucidates the role of any evolved CO2 on LIB degradation but also provides a new avenue for fundamental understanding of a much sought-after electrochemical reaction. The emerging insight gained through the use of this novel characterisation technique may be used to guide and validate battery lifetime models, as well as inform the development of ageing mitigation strategies, facilitating the commercialisation of better batteries with longer lifetimes.
Supporting Information: Probing degradation in lithium ion batteries with on-chip electrochemistry mass spectrometry
Experimental methods, EC-MS data processing, EC-MS cell design, modelling inputs and results, and additional EC-MS data.