TY - JOUR A1 - Winckelmann, Alexander A1 - Nowak, S. A1 - Richter, Silke A1 - Recknagel, Sebastian A1 - Riedel, Jens A1 - Vogl, Jochen A1 - Panne, Ulrich A1 - Abad Andrade, Carlos Enrique T1 - High-Resolution Atomic Absorption Spectrometry Combined With Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium N2 - 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. KW - Lithium KW - Isotope KW - Machine learning KW - Algorithms KW - Reference material KW - AAS KW - Atomic Absorption Spectrometry PY - 2021 DO - https://doi.org/10.1021/acs.analchem.1c00206 SN - 1520-6882 VL - 93 IS - 29 SP - 10022 EP - 10030 PB - American Chemical Society CY - Washington, DC, USA AN - OPUS4-53028 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Morcillo, Dalia T1 - Atomic absorption spectrometry with machine learning as a tool for lithium isotope analysis N2 - The general interest in the implementation of renewable energies, particularly in the development of lithium-ion batteries, makes lithium a key element to be analyzed. We are working to develop this tool to determine if the isotopic effect of lithium has an impact on battery aging. T2 - SALSA Konferenz CY - Berlin, Germany DA - 16.09.2021 KW - Atomic absorption spectrometry KW - Battery KW - Machine learning KW - Lithium KW - Isotope PY - 2021 AN - OPUS4-53693 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 -