The failure mechanism of silicon-based electrodes has been studied only in a half-cell configuration so far. Here, a combination of 7Li, 19F MAS NMR, XPS, TOF-SIMS, and STEM-EELS, provides an in-depth characterization of the solid electrolyte interphase (SEI) formation on the surface of silicon and its evolution upon aging and cycling with LiNi1/3Mn1/3Co1/3O2 as the positive electrode in a full Li-ion cell configuration. This multiprobe approach indicates that the electrolyte degradation process observed in the case of full Li-ion cells exhibits many similarities to what has been observed in the case of half-cells in previous works, in particular during the early stages of the cycling. Like in the case of Si/Li half-cells, the development of the inorganic part of the SEI mostly occurs during the early stage of cycling while an incessant degradation of the organic solvents of the electrolyte occurs upon cycling. However, for extended cycling, all the lithium available for cycling is consumed because of parasitic reactions and is either trapped in an intermediate part of the SEI or in the electrolyte. This nevertheless does not prevent the further degradation of the organic electrolyte solvents, leading to the formation of lithium-free organic degradation products at the extreme surface of the SEI. At this point, without any available lithium left, the cell cannot function properly anymore. Cycled positive and negative electrodes do not show any sign of particles disconnection or clogging of their porosity by electrolyte degradation products and can still function in half-cell configuration. The failure mechanism for full Li-ion cells appears then very different from that known for half-cells and is clearly due to a lack of cyclable lithium because of parasitic reactions occurring before the accumulation of electrolyte degradation products clogs the porosity of the composite electrode or disconnects the active material particles.

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.