TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Applications of atomic absorption spectrometry for lithium isotope analysis N2 - 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. T2 - Colloquium Spectroscopicum Internationale XLII (CSI XLII) CY - Gijón, Spain DA - 30.05.2022 KW - Lithium KW - HR-CS-AAS KW - Chemometrics KW - Atomic spectrometry PY - 2022 AN - OPUS4-56498 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - The use of reference materials to improve the calibration strategy in glow discharge optical emission spectroscopy with machine learning N2 - 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. T2 - Winter Conference on Plasma Chemistry 2022 CY - Tucson, AZ, USA DA - 17.01.2022 KW - GDOES KW - Machine learning KW - Reference materials KW - Calibration KW - Cooper PY - 2022 AN - OPUS4-56497 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Improvement of manufacturing processes of Li-ion batteries by Glow Discharge Optical Emission Spectroscopy N2 - Manufacturing lithium-ion coin cells (LIBs) for scientific research demands reproducibility, precision, and thorough metrology to ensure consistent quality and performance. Glow-discharge optical emission spectroscopy (GD-OES) emerges as a crucial analytical technique in this context, providing detailed insights into elemental composition and material homogeneity [1,2]. This study focuses on using GD-OES to optimize and standardize the manufacturing processes of LIBs, emphasizing metrology and traceability to develop reproducible and high-quality batteries for research purposes. We refined cell production by adjusting key parameters such as cathode doping, electrolyte concentration, and calendar pressing. GD-OES facilitated depth analysis of elemental composition and distribution, which is essential for identifying and maintaining optimal manufacturing conditions. Validation was achieved through electrochemical impedance spectroscopy (EIS), ensuring the quality and consistency of the manufactured batteries. GD-OES analysis revealed critical insights into elemental uniformity and impurities, guiding adjustments significantly improving cell performance and reproducibility. The method proved fast and effective in detecting and correcting variations in the manufacturing process, leading to enhanced battery quality. To understand the aging mechanisms, GD-OES was employed for fluorine depth profiling, which is crucial for studying polymer and electrolyte degradation. We significantly enhanced fluorine detection sensitivity by substituting the argon plasma with a neon/argon mixture, providing a more accurate aging analysis. By demonstrating the versatility and efficacy of GD-OES in optimizing LIB manufacturing processes and gaining deeper insights into aging mechanisms, this research has significant practical implications. It not only advances lab research but also offers tangible industrial benefits, including improved battery quality, extended lifespan, and enhanced performance. By ensuring metrology and traceability, GD-OES contributes to developing a robust method for the reproducible manufacturing of Li-ion coin cells, boosting innovation and sustainability in battery technology. T2 - SciX 2025 CY - Covington, KY, USA DA - 05.10.2025 KW - GD-OES KW - Battery analysis KW - Depth profile KW - Lithium-ion battery PY - 2025 AN - OPUS4-64878 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Tracking Lithium-Ion Battery Ageing via Lithium Isotope Fractionation N2 - Lithium-ion batteries power portable devices, electric vehicles, and stationary power grids, yet hidden aging reactions still shorten their service life and raise concerns about cost and safety. An analytical proxy is needed to report these reactions and accelerate product development, quality control, and recycling. Here, we demonstrate that subtle shifts in the natural 7Li/6Li ratio accurately record the two decisive stages of cell aging, solid-electrolyte interphase (SEI) formation and field-driven aging, which can be detected using multi-collector ICP-MS. Because Li is easily stripped from digested electrodes or electrolyte in a one-step cation-exchange column, high-purity solutions reach the spectrometer in minutes. A streamlined MC-ICP-MS run yields a δ7LiLSVEC precision of 0.4 ‰, enabling dozens of battery fractions to be analyzed per day. Applying the workflow to LiCoO2 coin cells as models, sampled from pristine to 700 cycles, reveals a clear isotopic narrative. During the first ≈45 cycles, 7Li leaves the LiCoO2 lattice, dissolves into the electrolyte, and is locked in the SEI on graphite, driving cathode δ7Li from +8 to –10 ‰ and raising the anode to +13 ‰ while capacity drops by 10 %. After the interphase matures, the electric field takes over: the lighter 6Li migrates faster to the anode, 7Li accumulates in the contracting Li1-xCoO2 lattice, and the bulk separation factor rises to α≈1.045 by 700 cycles. The δ7Li curve flattens roughly 70 cycles before capacity falls to 80%, providing an early warning of end-of-life. Isotopic gradients scale linearly with impedance growth, SEI thickness, and crack density confirmed by LA-ICP-MS mapping, FIB-SEM, XANES, and EXAFS. Although each data point requires one cell, lithium-isotope fractionation provides direct, element-specific, and structural fatigue insight unavailable from non-destructive tests. The straightforward chemistry and fast MC-ICP-MS routine make the approach practical for targeted aging studies, additive screening, and forensic autopsies, complementing high-throughput electrochemical methods and supporting the design of longer-lived batteries. T2 - SciX 2025 CY - Covington, KY, USA DA - 05.10.2025 KW - Isotope KW - Lithium KW - MC-ICP-MS KW - MICAP-MS KW - Isotope fractionation KW - Battery PY - 2025 AN - OPUS4-64879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -