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Lithium-ion batteries (LIBs) are one technology to overcome the challenges of climate and energy crisis. They are widely used in electric vehicles, consumer electronics, or as storage for renewable energy sources. However, despite innovations in batteries' components like cathode and anode materials, separators, and electrolytes, the aging mechanism related to metallic aluminum current collector degradation causes a significant drop in their performance and prevents the durable use of LIBs. Glow-discharge optical emission spectroscopy (GD-OES) is a powerful method for depth-profiling of batteries' electrode materials. This work investigates aging-induced aluminum deposition on commercial lithium cobalt oxide (LCO) batteries' cathodes. The results illustrate the depth-resolved elemental distribution from the cathode surface to the current collector. An accumulation of aluminum is found on the cathode surface by GD-OES, consistent with results from energy-dispersive X-ray spectroscopy (EDX) combined with focused ion beam (FIB) cutting. In comparison to FIB-EDX, GD-OES allows a fast and manageable depth-profiling. Results from different positions on an aged cathode indicate an inhomogeneous aluminum film growth on the surface. The conclusions from these experiments can lead to a better understanding of the degradation of the aluminum current collector, thus leading to higher lifetimes of LIBs.
Lithium-ion batteries (LIBs) are one technology to overcome the challenges of climate and energy crisis. They are widely used in electric vehicles, consumer electronics, or as storage for renewable energy sources. However, despite innovations in batteries' components like cathode and anode materials, separators, and electrolytes, the aging mechanism related to metallic aluminum current collector degradation causes a significant drop in their performance and prevents the durable use of LIBs.[1] Glow-discharge optical emission spectroscopy (GD-OES) is a powerful method for depth-profiling of batteries' electrode materials. This work investigates aging-induced aluminum deposition on commercial lithium cobalt oxide (LCO) batteries' cathodes. The results illustrate the depth-resolved elemental distribution from the cathode surface to the current collector. An accumulation of aluminum is found on the cathode surface by GD-OES, consistent with results from energy-dispersive X-ray spectroscopy (EDX) combined with focused ion beam (FIB) cutting. In comparison to FIB-EDX, GD-OES allows a fast and manageable depth-profiling. Results from different positions on an aged cathode indicate an inhomogeneous aluminum film growth on the surface. The conclusions from these experiments can lead to a better understanding of the degradation of the aluminum current collector, thus leading to higher lifetimes of LIBs.
Lithium-ion batteries are a key technology for tackling challenges in energy and climate crisis, but up to 30 % are discarded right after production. Quality is closely related to the homogeneity of the used materials and coatings. Fluorine compounds, both in the polymer and from degradation of the electrolyte, are of special interest for the formation and aging of LIBs. Glow-discharge optical emission spectroscopy (GD-OES) emerged as a fast and convenient method for depth-profiling of battery materials. However, fluorine remains a spectroscopic challenge due to a high excitation energy and only few strong emission lines in the UV/Vis spectral range.
We investigated the partial substitution of argon with neon in the plasma gas. The main emission line at 685.603 nm was monitored by both photo multiplier tube (PMT) with Czerny-Turner monochromator at 20 µm slit-size, and charge-coupled device (CCD) with 1200 lines/mm grating. For calibration, we used a set of hot-pressed copper cylinders with varying calcium fluoride amount fraction, ranging from 1.6 % to 4.4 %. Plasma gas mixtures with a mole fraction of 5 %, 10 % and 20 % neon in argon were used. Sensitivity in comparison to pure argon was increased by factor 4, 7 and 14, respectively. In general, PMT detection was more sensitive than CCD. As expected, sputter rates decreased with higher neon content in the plasma gas. Depth-profiling of the cathodes was performed in pulsed mode at constant voltage and pressure, which resulted in stable plasma conditions. We achieve matrix-independent quantitative information on fluorine distribution by correction based on sputter rates.
This advancement in GD-OES provides a more accurate analytical approach for evaluating the homogeneity of lithium-ion battery materials, potentially leading to more sustainable and cost-effective manufacturing processes.
An alternative method for lithium isotope amount ratio analysis is proposed by combining atomic absorption spectrometry with spectra data analysis by machine leaning. It is based on the well-known isotope shift of around 15 pm for the electronic transition at wavelength 670.7845 nm which can be measured by a high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS). For isotope amount ratio analysis, a scalable three boosting machine learning algorithm (XGBoost) was employed and calibrated with a set of samples with a 6Li isotope amount fraction ranging from 99% to 6%. The absolute Li isotope amount fractions of these calibration samples were previously measured by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) and used as ab-initio data for the machine learning algorithm. Validation of the machine leaning model was performed with two standard reference materials (LSVEC and IRMM-016). The procedure was employed for the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, LiOH, and LiF) as well as a BAM candidate LiMNC cathode reference material. Achieved uncertainties are one order of magnitude higher than those obtained by MC-ICP-MS. This precision and accuracy is nonetheless sufficient to resolve natural occurring variations in Lithium isotope ratios. Also, the LiMNC material was analyzed by HR-CS-AAS with and without matrix purification. The results are comparable within statistical error.