TY - CONF A1 - Winckelmann, Alexander T1 - High-resolution optical spectrometry for lithium isotope ratio analysis N2 - 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. T2 - Groupseminar - GFZ Potsdam CY - Online Meeting DA - 27.10.2020 KW - Lithium batteries KW - HR-CS-AAS KW - Machine learning KW - Isotope analysis PY - 2020 AN - OPUS4-51572 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Isotopic analysis by high-resolution optical spectroscopy N2 - Isotope analysis can be used to determine the age and provenance of geological samples or to study dynamic systems like Li-ion batteries. Modern techniques in optical spectrometry allow us a fast and slow-cost isotope analysis. These techniques include high-resolution continuum source molecular absorption spectrometry (HR-CS-MAS) and laser ablation molecular isotopic spectrometry. This seminar will discuss our recent work on precise and accurate isotope analysis of boron, magnesium, and lithium with geology, climate, and energy research applications. T2 - Recent Trends in Chemical Science and Technology, Christian College Chengannur, India CY - Online meeting DA - 29.09.2020 KW - Isotopes KW - Optical spectrometry KW - Machine learning PY - 2020 AN - OPUS4-51997 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - JOUR A1 - Morcillo, Dalia A1 - Winckelmann, Alexander A1 - Frick, Daniel A. A1 - Jacobsen, Lars A1 - Seger, Tino A1 - Florek, Stefan A1 - Richter, Silke A1 - Vogl, Jochen A1 - Recknagel, Sebastian A1 - Panne, Ulrich A1 - Abad Andrade, Carlos Enrique T1 - Lithium isotope ratio analysis of geological samples using atomic absorption spectrometry with improved spectral resolution N2 - This study introduces an improved spectrometric method with enhanced precision to determine isotope ratios in geological samples without chromatographic separation. Firstly, the improvement is achieved by increasing the spectral resolution of the spectrometer applied in well-known high-resolution continuum source atomic absorption spectrometry (HR-CS-AAS). The resulting resolving power and linear dispersion of the upgraded setup, which is denoted in the following as HR+CS-AAS, is well adapted to the line widths of the Li isotope components we investigated. Secondly, our proposed method combines optical absorption spectrometry with machine learning data analysis using an extreme gradient boosting algorithm (XGBoost). This method was applied to analyze certified geological reference materials with δLSVEC(7Li/6Li) (hereafter δ7Li) values ranging from −0.5 ‰ to 4.5 ‰. With a pixel related optical resolving power of λ/∆λ ≈ 780 000, we obtain precisions in δ7Li measurements from 1.0 ‰ to 2.5 ‰. The method is validated by comparing the results with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), confirming its metrological compatibility. This work presents a fast, robust, and reliable method for δ7Li measurement in geological samples. KW - Isotope KW - High-resolution continuum source atomic absorption spectroscopy KW - Lithium KW - Machine learning KW - Echelle spectrometer PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-611807 DO - https://doi.org/10.1016/j.sab.2024.107013 VL - 220 SP - 1 EP - 5 PB - Elsevier BV AN - OPUS4-61180 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 - You, Zengchao T1 - Chemometrical analysis of spectral data obtained from glow discharge optical emission spectroscopy for elemental analysis of metals N2 - The poster shows the combination of GD-OES and machine learning. The gola of this project is to establish a new and robust calibration model, which can be used to identify elemental composition and concentration of metals from a single spectra. T2 - Salsa make and measure Konferenz CY - Online meeting DA - 16.09.2021 KW - Glow discharge optical emission spectroscopy KW - Machine learning PY - 2021 AN - OPUS4-53323 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Morcillo, Dalia T1 - Lithium isotope analysis combining atomic absorption spectroscopy and machine learning N2 - 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. T2 - SCIX Conference CY - Providence, RI, USA DA - 26.09.2021 KW - Lithium isotope KW - High-resolution continuum source atomic absorption spectrometer KW - Machine learning PY - 2021 AN - OPUS4-53686 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 - Winckelmann, Alexander T1 - Next level lithium isotope analysis by atomic absorption spectrometry in combination with machine learning N2 - 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. T2 - Make and Measure 2020 CY - Online Meeting DA - 15.10.2020 KW - Lithium batteries KW - HR-CS-AAS KW - Machine learning KW - Isotope analysis KW - Spectrometry PY - 2020 AN - OPUS4-51541 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Morcillo, Dalia T1 - High-resolution absorption isotopic spectrometry as a tool for aging studies of Li-ion batteries N2 - Lithium (Li) is the key element in the manufacturing of batteries. Isotopic study of Li may help to identify the causes of battery aging due to isotopic fractionation during charge/discharge cycles. Isotopic ratio determination is based on monitoring the isotopic components of lithium by their spin-orbit coupling and its isotopic shift of about 15 pm for the 22P←22S electronic transition around 670.788 nm. In this work, we propose improvements to our previous work [1] by using a higher-resolution double echelle modular spectrometer (HR-DEMON II) coupled to a continuum source graphite furnace atomic absorption spectrometer (HR-CS-GF-AAS) for the isotopic analysis of Li. The data analysis was carried out by using a decision-tree-based ensemble machine learning (ML) algorithm (XGBoost). A set of samples with 6Li isotope amount fractions ranging from 0.0004 to 0.99 mol mol-1 was used for the algorithm's training. Subsequently, the procedure was validated by a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, a cathode material (NMC111). Finally, the ML model was applied to determine the isotope ratio of geological samples, including anorthosite, granite, soil, rhyolite, nepheline syenite, and basalt and battery samples. These samples were measured as digested without any further purification step. Improvements in the optical resolution resolve the lithium isotopic components of the atomic spectra. In the studied geological samples, were found δ7Li values between -0.5 and 4.5 ‰ with a precision range of 1 to 2 ‰. In addition, the proposed method was validated with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), and these results are comparable and compatible. T2 - Caltech-BAM Meeting CY - Online meeting DA - 10.08.2022 KW - Lithium isotope KW - Machine learning KW - Battery KW - High-resolution absorption isotopic spectrometry PY - 2022 AN - OPUS4-56380 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -