TY - CONF A1 - Winckelmann, Alexander A1 - Abad Andrade, Carlos Enrique A1 - Richter, Silke A1 - Recknagel, Sebastian A1 - Riedel, Jens A1 - Vogel, Jochen A1 - Panne, Ulrich 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 -