Chemische Charakterisierung und Spurenanalytik
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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.
Various applications for artificial intelligence in the context of spectroscopy will be presented. in particular, examples from BAMline will be presented. After a short introduction to synchrotron radiation, artificial intelligence algorithms for the quantification of X-ray fluorescence measurement are discussed. In the second example, information retrieval by natural language processing is discussed.
In this talk, the features of X-ray fluorescence analysis with synchrotron radiation will be presented. First, the basics of the origin of synchrotron radiation and X-ray fluorescence analysis and the experimental setup will be discussed. Then, examples of trace element detection, micrometer resolution, and application of the X-ray color camera will be shown.
Various applications for artificial intelligence in the context of spectroscopy will be presented. in particular, examples from bamline will be featured. After a short introduction to synchrotron radiation, artificial intelligence algorithms for the quantification of X-ray fluorescence measurement are discussed. In the second example, information retrieval by natural language processing is reviewed. As a last example the reconstruction of measurements with the X-ray color camera and coded apertures is presented.
In this work, we describe the use of artificial neural networks for the quantification of X-ray fluorescence measurements. The training data were generated using Monte Carlo simulation, which avoided the use of adapted reference materials. The extension of the available data set by means of an ANN to generate additional data was demonstrated. Particular emphasis was put on the comparability of simulated and experimental data and how the influence of deviations can be reduced. The search for the optimal hyperparameter, manual and automatic, is also described. For the presented case, we were able to train a network with a mean absolute error of 0.1 weight percent for the synthetic data and 0.7 weight percent for a set of experimental data obtained with certified reference materials.
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.
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.