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What does GD-OES reveal about the aging and manufacturing processes of lithium-ion batteries?
(2024)
Glow-Discharge Optical Emission Spectroscopy (GD-OES), a powerful analytical technique, sheds light on the two critical aspects of lithium-ion batteries (LIBs): manufacturing and aging 1, 2. We optimized cell production in manufacturing by adjusting parameters, including cathode doping, electrolyte concentration, and pressing force. GD-OES provided in-depth elemental composition and homogeneity analysis, which is crucial for identifying optimal manufacturing conditions. These findings were validated by electrochemical impedance spectroscopy, confirming the quality of the manufactured batteries.
Shifting the focus to aging, we use GD-OES for fluorine depth profiling, a key element in understanding polymer and electrolyte degradation. However, fluorine presents analytical challenges. We addressed this by substituting argon with a neon:argon mixture, which significantly enhanced fluorine detection sensitivity. This advancement not only improves accuracy but also holds the potential to guide sustainable and cost-efficient manufacturing strategies.
Through its versatility, GD-OES has proven to be a powerful tool for not only optimizing LIB manufacturing processes but also gaining deeper insights into their aging mechanisms. This research extends beyond academic interest, offering tangible benefits for the industry by translating into improved battery quality, extended lifespan, and overall performance.
High-resolution absorption isotopic spectrometry as a tool for aging studies of Li-ion batteries
(2022)
Lithium (Li) is the key element in the manufacturing of batteries. Isotopic study of Li may help to identify the causes of battery aging due to isotopic fractionation during charge/discharge cycles.
Isotopic ratio determination is based on monitoring the isotopic components of lithium by their spin-orbit coupling and its isotopic shift of about 15 pm for the 22P←22S electronic transition around 670.788 nm. In this work, we propose improvements to our previous work [1] by using a higher-resolution double echelle modular spectrometer (HR-DEMON II) coupled to a continuum source graphite furnace atomic absorption spectrometer (HR-CS-GF-AAS) for the isotopic analysis of Li.
The data analysis was carried out by using a decision-tree-based ensemble machine learning (ML) algorithm (XGBoost). A set of samples with 6Li isotope amount fractions ranging from 0.0004 to 0.99 mol mol-1 was used for the algorithm's training. Subsequently, the procedure was validated by a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, a cathode material (NMC111). Finally, the ML model was applied to determine the isotope ratio of geological samples, including anorthosite, granite, soil, rhyolite, nepheline syenite, and basalt and battery samples. These samples were measured as digested without any further purification step.
Improvements in the optical resolution resolve the lithium isotopic components of the atomic spectra. In the studied geological samples, were found δ7Li values between -0.5 and 4.5 ‰ with a precision range of 1 to 2 ‰. In addition, the proposed method was validated with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), and these results are comparable and compatible.