Chemische Charakterisierung und Spurenanalytik
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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.
An alternative method for lithium isotope analysis by using high-resolution atomic absorption spectrometry (HR-CS-AAS) is proposed herein. This method is based on monitoring the isotope shift of approximately 15 pm for the electronic transition 22P←22S at around the wavelength of 670.8 nm, which can be measured by state-of-the-art HR-CS-AAS. Isotope analysis can be used for (i) the traceable determination of Li concentration and (ii) isotope amount ratio analysis based on a combination of HR-CS-AAS and spectral data analysis by machine learning (ML).
In the first case, the Li spectra are described as the linear superposition of the contributions of the respective isotopes, each consisting of a spin-orbit doublet, which can be expressed as Gaussian components with constant spectral position and width and different relative intensity, reflecting the isotope ratio in the sample. Precision was further improved by using lanthanum as internal spectral standard. The procedure has been validated using human serum-certified reference materials. The results are metrologically comparable and compatible with the certified values.
In the second case, for isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol mol−1. The training ML model was validated with certified reference materials. The procedure was applied to the isotope amount ratio determination of a set of stock chemicals and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. These determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰, and the precision obtained ranged from 1.9‰ to 6.2‰. This precision was sufficient to resolve naturally occurring variations. The NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification to assess its suitability for technical applications. The results obtained were metrologically compatible with each other.