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 - Optical spectrometry for isotope analysis N2 - Isotope analysis plays a critical role in various disciplines, including environmental science, archaeology, and forensic investigations. Traditional methods such as mass spectrometry provide precise isotopic data but often require complex, costly setups and extensive sample preparation. As an alternative, optical spectrometry has emerged as a versatile and less invasive technique. This presentation explores the advancements and applications of optical spectrometry methods in isotope analysis, emphasizing their benefits and challenges. T2 - University of Calgary PHYS 561 - Stable And Radioactive Isotope - Winter 2024 CY - Online meeting DA - 07.03.2024 KW - Isotopes KW - HR-CS-MAS KW - Chemometrics KW - Laser Ablation Molecular Absorption spectrometry KW - LAMIS PY - 2024 AN - OPUS4-59948 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Isotope analysis through the integration of chemometrics into optical spectroscopy N2 - Lithium (Li), Boron (B), Nitrogen (N), Magnesium (Mg), and Calcium (Ca) are pivotal elements across various spheres such as the hydrosphere, biosphere, and lithosphere, significantly impacting (bio-) geochemical and physiological processes. These elements exhibit stable isotopes with substantial roles in geological, environmental, and biological studies. The traditional method for measuring isotope amount ratios has been through mass spectrometry, which, despite its accuracy, comes with high operational costs, the need for skilled operators, and time-consuming sample preparation processes. Combining optical spectroscopy with chemometrics introduces an innovative, cost-effective approach by the hand of high-resolution continuum source atomic and molecular absorption spectrometry (HR-CS-AAS and HR-CS-MAS) for the analysis of isotope ratios in Li, B, N, Mg, and Ca. By analyzing the atomic or molecular absorption spectrum of the in-situ generated cloud of atoms of diatomic molecules (e.g., Li, BH, NO, MgF, CaF) during the electronic transition from the fundamental state, this method allows for the rapid determination of isotope ratios directly from sample solutions without the need for complex sample preparation. For each element, the respective atomic or molecule's absorption spectrum was deconvoluted into its isotopic components using partial least squares regression or machine learning algorithms. Robust calibration models were developed, calibrated with enriched isotope, and validated against certified reference materials. Spectral data underwent preprocessing to optimize the modeling to determine the optimal number of latent variables. The findings showcase that this optical spectrometric method yields results that agree with those obtained via inductively coupled plasma mass spectrometry (ICP-MS), offering a promising, cost-effective, and rapid alternative for isotope analysis with precisions as low as ± 0.2‰. This approach is a significant advancement in analytical chemistry, providing a new way to study isotope variations in biological, environmental, and geological samples. T2 - Analytica Conference CY - Munich, Germany DA - 09.04.2024 KW - Isotopes KW - HR-CS-MAS KW - Chemometrics KW - Lithium KW - Boron KW - Magnesium KW - Nitrogen PY - 2024 AN - OPUS4-59946 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Paul, Andrea T1 - Potential of multivariate data analysis (MVA) in X-ray fluorescence analysis N2 - X-ray fluorescence spectrometry (XRF) is used, for example, in geochemistry, archaeology, materials science and agriculture to analyze the elemental composition of various samples [1]. Quantitative spectrum analysis typically applies methods such as fundamental parameters or intensity correction [1, 2]. However, there are also approaches to analyze XRF spectra using data-driven MVA approaches that are not based on a physical model. Principal component analysis (PCA) enables the visualization of data and the discovery of hidden relationships between analytical signals and sample parameters [1, 3]. Classification methods, e.g. PCA, cluster or discriminant analysis, can help to distinguish samples based on various complex integral features such as authenticity or origin of the sample [3]. MVA can also be used to correlate various external factors with elemental composition. In addition to classifying mineral wool into different material types based on characteristic oxides, PCA also enables the visualization of problem cases and supports the detection of possible outliers [4]. Multivariate regression methods such as partial least squares regression (PLSR) are a powerful tool for the quantitative analysis of spectra. In contrast to univariate analysis, in which only one characteristic is used as the basis for the regression, entire spectra or spectral ranges are analyzed here. It has been shown that the quantitative determination of C, H, N and O in polymers based on WDXRF spectra can be achieved by PLSR [5]. However, in the presence of complex matrix effects, the limitations of PLSR become clear, as was shown in a reference case (EDXRF spectra of steel and ore samples) [2]. Here, PLSR still outperformed the fundamental parameter approach, but proved to be less accurate than an intensity correction approach. Neural networks, which are better able to deal with nonlinear effects, are only an alternative if both a large number of calibration samples are available for adequate network training and a lot of time is available for network optimization. Despite limitations, it can be summarized that MVA, if used correctly, can contribute to increasing the informative value of XRF and to opening up new fields of application in areas of growing economic importance [1-5]. T2 - 12. PRORA Fachtagung Prozessnahe Röntgenanalytik CY - Berlin, Germany DA - 28.11.2024 KW - Chemometrics KW - Mineral wool KW - Data analysis KW - X-ray fluorescence PY - 2024 AN - OPUS4-62032 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -