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A novel simple synthetic pathway to a carbon-coated Li2S material from a single molecular source for battery application using electrospinning methods is presented.

In order to optimize the electron transfer between the Li4Ti5O12-based active mass and the current collector, the surface of aluminum foil was modified either by alkaline etching or by a carbon coating. The as-modified aluminum foils were coated with an active mass of Li4Ti5O12 mixed with polyvinylidene fluoride, sodium carboxymethyl cellulose, or polyacrylic acid as binders. Untreated aluminum and copper foils served as reference current collectors. The corrosion reactions of aluminum foil with the applied binder solutions were studied and the electrode structure has been analyzed, depending on the binder. Finally, the electrochemical performance of the prepared electrodes was investigated. Based on these measurements, conclusions concerning the electrical contact between the different current collectors and the active masses were drawn. The energy density of the Li4Ti5O12 electrodes cast on carbon-coated aluminum foils was significantly increased, compared to the corresponding electrodes with a copper current collector

Lithium‐sulfur batteries have the potential to replace lithium‐ion batteries in the future due to their high theoretical capacity and energy density but suffer from low cycling stability caused by the polysulfide shuttle. This work demonstrates a reduction of the polysulfide shuttle and increased cycling stability by using iron oxide and chromium oxide as additives for a simply fabricated lithium sulfide cathode. Adsorption isotherms were recorded, and monolayer adsorption capacities were determined for a better understanding of the interactions between transition metal oxides and polysulfides. A significant reduction of the shuttle mechanism can be deduced from the cyclization experiments. Similarly, the catalytic influence of chromium oxide and iron oxide on the oxidation could be shown by cyclic voltammetry.

This work shows the application of artificial neural networks in terms of modeling and simulating the aging process and the degradation of proton exchange membrane water electrolysis stacks. It includes the training process based on extracted measurement data, the evaluation, and the extrapolation of the network. The fundamentals of the utilized artificial neural network and the training algorithm are clarified. Next, the principle degradation effects are presented as well as the methodology of the underlying measurements. The resulting degradation of the electrolysis stack for different operation conditions is shown.