Chemische Charakterisierung und Spurenanalytik
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Isotope analysis is a tool for material research. For example, it may provide information about the provenance of a sample or changes in dynamic systems. Here is presented optical spectroscopy as an analytical alternative to mass spectrometry for isotope quantification based on the isotopic shift of atoms and diatomic molecules.
Der Bunsen-Kirchhoff-Preis 2022 wurde am 23.06.2022 anlässlich der analytica conference in München an Dr. Carlos Abad verliehen - in Anerkennung seiner exzellenten Entwicklungen im Bereich der continuum source atomic absorption spectrometry (CS-AAS).
Dr. Carlos Abad ist ein herausragender Experte auf dem Gebiet der Atom- und molekularen Absorptionsspektrometrie. insbesondere trug er maßgeblich zur substanziellen Weiterentwicklung von Echelle-Spektrometern für die CS-AAS bei. So gelang es, einen quantitativen Zugang zu Elementen wie Bor, Chlor, Fluor und Schwefel, mittels AAS zu erreichen. Erstmals demonstriert Dr. Carlos Abad am Beispiel eines Zr-Modifier, dass durch die Zeitauflösung der eingesetzten Echelle-Systeme mechanistische Untersuchungen zur Wirkung des Modifiers im Graphitrohrofen möglich sind.
Besonders hervorzuheben sind seine Arbeiten zum Einsatz der CS-AAS für die Analyse von Isotopen, die eine Genauigkeit aufweist, welche an die der Multikollektor-induktiv gekoppelten Plasma-Massenspektrometrie (MC-ICP-MS) heranreicht. Damit ergeben sich völlig neue Einsatzmöglichkeiten für technologisch hochrelevante Applikationen, wie z.B. die Untersuchung der Alterung von Lithium-Batterien oder die Lithium-Analyse in Blutserum.
Simultaneous multielemental analysis of crude oils by high-resolutions absorption spectrometry
(2019)
When crude oil arrives at a refinery it needs a lot of processing before it is suitable for cracking into lighter fractions. Sulfur has to be extracted to meet ultra-low sulfur legislation for most of the fuel grades, and desalination is a crucial process as chlorine within salts is corrosive to refinery equipment. Measuring the amounts of sulfur and chlorine within crude oil is the first step in a complex clean-up process.
Heavy metals, such as vanadium, nickel and iron need to be removed too. These metals can poison the catalyst used to crack the oil into lighter fractions. This is costly as it reduces the useful life of the catalyst. Like chlorine, heavy metals also have a corrosive effect on the plant. There is some evidence that the presence of some metals in the final product can reduce performance.
A fast determination of elements and size of suspended particles is vital for diagnosis and safeguard of refinery equipment. However, sample preparation for current analytical methods consumes precious time and lost particle size information.
High-resolution continuum source graphite furnace absorption spectrometry (HR-CS-GFAS) is proposed as a fast analytical method for elemental determination in crude oils and potentially for simultaneous multielement and particle size analysis. This HR-CS-GFAS instrumentation is coupled to a modular simultaneous echelle spectrometer (MOSES) and provides a full optical window with high resolution (from 180 to 900 nm with bandwidth λ/170,000). By using this set-up, it is possible to generate a multiparameter 3D spectral image (atomic and molecular lines, isotopic shift, atomization delay, and intensities). The generated spectral images can be analyzed by multivariate regressions for the elemental and particle size analysis. Additionally, if several atomic and molecular lines are simultaneously measured, they may be used in multi-energy calibration (MEC), a method based on the use of many wavelengths of the same absorbing/emitting entity to improve the accuracy in analytical atomic spectrometry. This MEC approach combined with multivariate image analysis allows the quantification of selected elements (Si, Al, Ni, Fe, V, S, and Cl) and overcomes matrix effects.
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.
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.