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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.
In this work, we employed glow discharge optical emission spectrometry (GD-OES) depth profiling as a fast and semi-quantitative method to investigate the aluminum (Al) current collector degradation in commercial lithium cobalt oxide (LCO) pouch cells with no Al2O3 pretreatment. After battery aging, a heterogeneous deposit was found on the surface of the cathode. Gray hotspot areas within an extensive pale white region were formed. Consistent with energy dispersive X-ray (EDX) analysis of micro-cross sections milled via targeted focused ion beam (FIB), an Al-containing layer of approximately 3 µm can be observed using GD-OES. We attribute one main cause of this layer is the degradation of the Al current collector. The nonuniform growth of this layer was investigated by performing GD-OES depth profiling at different in-plane positions. We found that the gray area has a higher mass concentration of Al, probably in metallic form, whereas the white area was probably covered more homogeneously with Al2O3, resulting from the inhomogeneous distribution of the pitting positions on the current collector. Compared to FIB-EDX, GD-OES enables a faster and more convenient depth profile analysis, which allows the more productive characterization of lithium-ion batteries (LIBs), and consequently benefits the development of preferable battery materials.
During its 25 years of existence, the Inorganic Analysis Working Group of the Consultative Committee for Amount of Substance: Metrology in Chemistry and Biology (CCQM IAWG) has achieved much in establishing comparability of measurement results. Impressive work has been done on comparison exercises related to real-world problems in fields such as ecology, food, or health. In more recent attempts, measurements and comparisons were focused on calibration solutions which are the basis of most inorganic chemical measurements. This contribution deals with the question of how to achieve full and transparent SI traceability for the values carried by such solutions. Within this framework, the use of classical primary methods (CPMs) is compared to the use of a primary difference method (PDM). PDM is a method with a dual character, namely a metrological method with a primary character, based on the bundling of many measurement methods for individual impurities, which lead to materials with certified content of the main component. As in classical methods, where small corrections for interferences are accepted, in PDM, many small corrections are bundled. In contrast to classical methods, the PDM is universally applicable to all elements in principle. Both approaches can be used to certify the purity (expressed as mass fraction of the main element) of a high-purity material. This is where the metrological need of National Metrology Institutes (NMIs) for analytical methods meet the challenges of analytical methods. In terms of methods, glow discharge mass spectrometry (GMDS) with sufficient uncertainties for sufficiently small impurity contents is particularly noteworthy for the certification of primary transfer standards (PTS), and isotope dilution mass spectrometry (IDMS), which particularly benefits from PTS (back-spikes) with small uncertainties, is particularly noteworthy for the application. The corresponding relative uncertainty which can be achieved using the PDM is very low (< 10−4). Acting as PTS, they represent the link between the material aspect of the primary calibration solutions and the immaterial world of the International System of Units (SI). The underlying concepts are discussed, the current status of implementation is summarised, and a roadmap of the necessary future activities in inorganic analytical chemistry is sketched. It has to be noted that smaller measurement uncertainties of the purity of high-purity materials not only have a positive effect on chemical measurements, but also trigger new developments and findings in other disciplines such as thermometry or materials science.
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.
Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
Current developments in PT
(2021)
In this poster for the SCIX Conference, we apply an analytical procedure based on the monitoring of the lithium isotope through the partially resolved isotope shift in the electronic transition 22P<-22S around wavelength 670.80 nm using high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS) in combination with machine learning (ML) for the determination of Li Isotope ratio analysis.
The therapeutic dose of lithium (Li) compounds, which are widely used for the treatment of psychiatric and hematologic disorders, is close to its toxic level; therefore, drug monitoring protocols are mandatory. Herein, we propose a fast, simple, and low-cost analytical procedure for the traceable determination of Li concentration in human serum, based on the monitoring of the Li isotope dilution through the partially resolved isotope shift in its electronic transition around 670.80 nm using a commercially available high-resolution continuum source graphite furnace atomic absorption spectrometer. With this technique, serum samples only require acidic digestion before analysis. The procedure requires three measurements—an enriched 6Li spike, a mixture of a certified standard solution and spike, and a mixture of the sample and spike with a nominal 7Li/6Li ratio of 0.82. Lanthanum has been used as an internal spectral standard for wavelength correction. The 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. Both the spectral constants and the correlation between isotope ratio and relative band intensity have been experimentally obtained using commercially available materials enriched with Li isotopes. The Li characteristic mass (mc) obtained corresponds to 0.6 pg. The procedure has been validated using five human serum certified reference materials. The results are metrologically comparable and compatible to the certified values. The measurement uncertainties are comparable to those obtained by the more complex and expensive technique, isotope dilution mass spectrometry.
An alternative method for lithium isotope amount ratio analysis based on a combination of high-resolution atomic absorption spectrometry and spectral data analysis by machine learning (ML) is proposed herein. It is based on the well-known 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 the state-of-the-art high-resolution continuum source graphite furnace atomic absorption spectrometry. 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, previously determined by a multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied toward the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. The results of 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, as demonstrated for samples ranging from approximately −3 to +15‰. To assess its suitability to technical applications, the NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other.