Fast formation of anode-free Li-metal batteries by pulsed current.

Anode-free Li-metal batteries offer high energy density but are prone to dendrite formation during charging which can cause catastrophic failures. Ensuring dendrite-free smooth Li deposits during charging is therefore necessary. Suppressing dendrite growth can be achieved by pulsed current charging, especially during the formation cycle that largely determines the corrosion trajectory of a cell. As opposed to the constant-current technique, pulsed current techniques apply intermittently stopped current flows. This work investigates the electroplating of metallic Li onto a Cu foil current collector under constant-current and pulsed current formation protocols. In addition to smoother, less resistive electroplated metallic Li deposits and increased Coulombic efficiency, we show that by employing an optimized pulsed current formation protocol, the formation process is accelerated by a factor of 2 and the Coulombic efficiency was increased by 10% compared to a C/20 protocol. Finally, by employing a simple regression coupled to experimentation, we propose the pseudo-IR-drop to be used for live adjustment of pulsed current protocols i.e., individually approach each cell at all SOC during formation.

Conductivity experiments for electrolyte formulations and their automated analysis.

Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). The mass ratio of EC to PC was varied, while keeping the mass ratio of (EC + PC) and EMC at fixed values of 3:7 and 1:1. The conducting salt concentration was also varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments.

Correlating Oxygen Evolution Catalysts Activity and Electronic Structure by a High-Throughput Investigation of Ni1-y-zFeyCrzOx.

High-throughput characterization by soft X-ray absorption spectroscopy (XAS) and electrochemical characterization is used to establish a correlation between electronic structure and catalytic activity of oxygen evolution reaction (OER) catalysts. As a model system a quasi-ternary materials library of Ni1-y-zFeyCrzOx was synthesized by combinatorial reactive magnetron sputtering, characterized by XAS, and an automated scanning droplet cell. The presence of Cr was found to increase the OER activity in the investigated compositional range. The electronic structure of NiII and CrIII remains unchanged over the investigated composition spread. At the Fe L-edge a linear combination of two spectra was observed. These spectra were assigned to FeIII in Oh symmetry and FeIII in Td symmetry. The ratio of FeIII Oh to FeIII Td increases with the amount of Cr and a correlation between the presence of the FeIII Oh and a high OER activity is found.

Expediting Combinatorial Data Set Analysis by Combining Human and Algorithmic Analysis.

A challenge in combinatorial materials science remains the efficient analysis of X-ray diffraction (XRD) data and its correlation to functional properties. Rapid identification of phase-regions and proper assignment of corresponding crystal structures is necessary to keep pace with the improved methods for synthesizing and characterizing materials libraries. Therefore, a new modular software called htAx (high-throughput analysis of X-ray and functional properties data) is presented that couples human intelligence tasks used for "ground-truth" phase-region identification with subsequent unbiased verification by an algorithm to efficiently analyze which phases are present in a materials library. Identified phases and phase-regions may then be correlated to functional properties in an expedited manner. For the functionality of htAx to be proven, two previously published XRD benchmark data sets of the materials systems Al-Cr-Fe-O and Ni-Ti-Cu are analyzed by htAx. The analysis of ∼1000 XRD patterns takes less than 1 day with htAx. The proposed method reliably identifies phase-region boundaries and robustly identifies multiphase structures. The method also addresses the problem of identifying regions with previously unpublished crystal structures using a special daisy ternary plot.