Chemische Charakterisierung und Spurenanalytik
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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.
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.
The presentation gives an overview of BAM's activities on processing influences and application properties of MPEAs in the form of joined and machined high and medium entropy alloys (CoCrFeMnNi and CoCrNi). In the case of welding, the focus is on defect-free welded joints with sufficient mechanical properties. In the case of machining, the focus is on the possible influence on the surface quality of the materials through adequate milling parameters. In addition, the hydrogen absorption and diffusion properties as well as the electrochemical corrosion behavior are fundamentally examined.
The talk summarizes challenges in microplastic analysis. It shows the preparation of microplastic reference materials as well as the testing on homogeneity and stability. The reference material is used in an international laboratory comparison to compare different detection methods used for microplastic analysis. The methods used were µ-IR (FTIR+LDIR) for number-based methods and TED-GC/MS and Py-GC/MS for mass-based methods. The ILC was done under the umbrella of VAMAS TWA 45. Results of the participants are presented.
Mikroplastik Detektion mit Thermoanalytischen Methoden: Analytik, Referenzmaterial, Ringversuche
(2024)
Ich dem Vortrag geht es um die Vorstellung von thermoanalytischen Methoden für die Mikroplastik-Detektion. Verschiedene Kopplungsmöglichkeiten werden gezeigt und die Funktionsweise der TED-GC/MS wird erklärt. Im zweiten Teil werden Referenzmaterialien für die Mikroplastik-Analytik diskutiert. PET -Tabletten des PlasticTrace Projektes werden vorgestellt. Am Ende wird der VAMAS Ringversuch zur Mikroplastik-Detektion gezeigt.
Preliminary results of an interlaboratory comparison on microplastics organised by plasticsfate
(2023)
Microplastics are everywhere in the environment, but analytics is challenging. Since harmonisation is missing as well es suitable reference materials, BAM did under th umbrella of VAMAS funded by the EU Horizon 2020 project PlasticsFate a ILC for microplastic detection methods. Methods adressed were IR, Raman, Py-GC/MS and TED-GC/MS. The talk gives a first presentation and evaluation on the results.
Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
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.