TY - JOUR A1 - Li, Yue A1 - Colnaghi, Timoteo A1 - Gong, Yilun A1 - Zhang, Huaide A1 - Yu, Yuan A1 - Wei, Ye A1 - Gan, Bin A1 - Song, Min A1 - Marek, Andreas A1 - Rampp, Markus A1 - Zhang, Siyuan A1 - Pei, Zongrui A1 - Wuttig, Matthias A1 - Ghosh, Sheuly A1 - Körmann, Fritz A1 - Neugebauer, Jörg A1 - Wang, Zhangwei A1 - Gault, Baptiste T1 - Machine learning‐enabled tomographic imaging of chemical short‐range atomic ordering N2 - In solids, chemical short‐range order (CSRO) refers to the self‐organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning‐enhanced atom probe tomography (APT) can mine the near‐atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium‐entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state‐of‐the‐art Monte‐Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic‐scale architectures. KW - Chemical short-range order (CSRO) KW - Atom probe tomography (APT) KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623777 DO - https://doi.org/10.1002/adma.202407564 SN - 1521-4095 VL - 36 IS - 44 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62377 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Saeidfirozeh, H. A1 - Kubelík, P. A1 - Laitl, V. A1 - Krivkova, A. A1 - Vrabel, J. A1 - Rammelkamp, K. A1 - Schroder, S. A1 - Gornushkin, Igor B. A1 - Kepes, E. A1 - Zabka, J. A1 - Ferus, M. A1 - Porízka, P. A1 - Kaiser, J. T1 - Laser-induced breakdown spectroscopy in space applications: Review and prospects N2 - This review describes the principles and summarizes the challenges of analytical methods based on optical emission spectroscopy (OES) in space applications, with a particular focus on Laser-Induced Breakdown Spectroscopy (LIBS). Over the past decade, LIBS has emerged as a powerful analytical technique for space exploration and In-Situ Resource Utilization (ISRU) of celestial bodies. Its implementation has been suggested for various segments of the Space Resources Value Chain, including prospecting, mining, and beneficiation. Current missions to Mars, including the ChemCam instrument on the Curiosity rover, the SuperCam on the Perseverance rover, and the MarSCoDe on the Zhurong rover, are considered flagship applications of LIBS. Despite neither the Pragyan rover nor the Vikram lander waking from the lunar night, the success of the Chandrayaan-3 mission marks another milestone in the development of LIBS instruments, with further missions, including commercial ones, anticipated. This paper reviews the deployment of LIBS payloads on Mars rovers, upcoming missions prospecting the Moon and asteroids, and LIBS analysis of meteorites. Additionally, it highlights the importance of data processing specific to space applications, emphasizing recent trends in transfer learning. Furthermore, LIBS combined with other spectroscopic techniques (e.g., Raman Spectroscopy, Mass Spectrometry, and Fourier-Transform Infrared Spectroscopy) represents an intriguing platform with comprehensive analytical capabilities. The review concludes by emphasizing the significance of LIBS-based contributions in advancing our understanding of celestial bodies and paving the way for future space exploration endeavors KW - Laser-induced breakdown spectroscopy KW - Mars KW - Moon KW - Asteroids KW - Meteorites KW - Machine learning KW - Transfer learning KW - Raman spectroscopy KW - Mass spectrometry KW - Fourier-transform infrared spectroscopy PY - 2024 DO - https://doi.org/10.1016/j.trac.2024.117991 VL - 181 SP - 1 EP - 22 PB - Elsevier B.V. AN - OPUS4-62142 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique T1 - Data-Driven Spectrochemical Methods for Elemental and Isotopic Analysis N2 - High-resolution optical spectrometers generate spectra containing tens of thousands of data points per sample. Picometre-scale isotope shifts, matrix-induced line broadening, and strong inter-feature correlations render classical peak fitting unreliable. Current analytical challenges, therefore, require rigorous algorithms able to expose latent structure, quantify uncertainty, and remain chemically interpretable. The research program presented in this lecture integrates state-of-the-art spectrochemical instrumentation with mathematically disciplined data models. Principal Component Analysis and Partial Least Squares provide chemically meaningful latent variables, while gradient-boosted decision trees or deep neural networks (ANNDL) capture residual non-linearity without sacrificing traceability. All models are trained on isotope-enriched or synthetically generated spectra and distributed with full validation workflows. T2 - Chemisches Institutskolloquium, Humboldt-Universität zu Berlin CY - Berlin, Germany DA - 21.05.2025 KW - Spectrochemistry KW - Isotopes KW - Machine learning KW - Battery KW - Data fusion PY - 2025 AN - OPUS4-63488 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - Russo, Francesco F. A1 - Nowatzky, Yannek A1 - Jaeger, Carsten A1 - Parr, Maria K. A1 - Benner, Philipp A1 - Muth, Thilo A1 - Lisec, Jan T1 - Machine learning methods for compound annotation in non‐targeted mass spectrometry—A brief overview of fingerprinting, in silico fragmentation and de novo methods N2 - Non‐targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance–based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field.In this work, we illustrate which ML‐based methods are available for compound annotation in non‐targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies. KW - Mass Spectrometry KW - Spectra annotation KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-608732 DO - https://doi.org/10.1002/rcm.9876 VL - 38 IS - 20 SP - 1 EP - 15 PB - Wiley AN - OPUS4-60873 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 - 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 -