TY - JOUR A1 - Hoffmann, V. A1 - Uhlemann, M. A1 - Richter, Silke A1 - Pfeifer, jens T1 - Calibration capacity of hot-pressed hydrogen standards for glow discharge optical emission and mass spectrometry JF - Spectrochimica Acta Part B: Atomic Spectroscopy N2 - Mixed copper and titanium hydride powder was hot-pressed and characterized by Carrier Gas Hot Extraction, XRay Diffraction, Thermal Gravimetric Analysis coupled with Mass Spectrometry, and Scanning Electron Microscopy. The hot-pressed and five conventional samples were applied for calibration of hydrogen in Glow Discharge Optical Emission and Mass Spectrometry. Up to the introduction of 15 ng/s hydrogen the Emission yield model is useful in Glow Discharge Optical Emission Spectrometry. A correlation between saturation and even reversal of the emission yield of the spectral lines H121, H486 and H656 and low sputtering rates was found. Hydrogen effects exist for the spectral lines of Cu(II) 219 and Ti(I) 399. In Glow Discharge Mass Spectrometry, a linear dependency of the 1H ion current on the sputtered mass per time exists over the total range of hydrogen content investigated. Hydrogen effects also exist for the sensitivity of 48Ti and 63Cu. The sputtering rate of two-phase materials depends linearly on the sputtered mass per time of one phase, which allows the sputtering rate of two-phase materials with known composition to be predicted. KW - Hot-pressing KW - GD-OES KW - GD-MS KW - Calibration KW - Hydrogen KW - Titanium hydride KW - Sputtering KW - Two-phase system PY - 2021 DO - https://doi.org/10.1016/j.sab.2020.106039 VL - 176 SP - 106039 PB - Elsevier B.V. CY - Amsterdam AN - OPUS4-52074 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gonzales-Gago, C. A1 - Smid, P. A1 - Hofmann, T. A1 - Venzago, C. A1 - Hoffmann, V. A1 - Gruner, W. A1 - Pfeifer, Jens A1 - Richter, Silke A1 - Kipphardt, Heinrich T1 - Investigations of matrix independent calibration approaches in fast flow glow discharge mass spectrometry JF - JAAS N2 - The performance of glow discharge mass spectrometry (GD-MS) is investigated for the accurate quantification of metallic impurities and oxygen in solid samples using the fast flow source GD-MS instrument ELEMENT GD. Different quantification approaches based on relative and absolute sensitivity factors are evaluated for the determination of metallic impurities using three sample matrixes (Al, Cu and Zn). The effect of the discharge conditions (voltage, current, discharge gas pressure/flow) on the sensitivity is investigated and the parameters are optimized to favour matrix independent calibrations. Improved standard relative sensitivity factors (StdRSFs) are calculated under optimal conditions based on multi-matrix calibrations. The sputtering rate corrected calibration is also presented as a multi-matrix calibration approach. The capabilities of GD-MS for oxygen determination are also investigated using a set of new conductive samples containing oxygen with mass fractions in the percent range in three different matrices (Al, Mg and Cu) produced by a sintering process. Poor limits of detection (in the order of g/kg) were obtained as consequence of the reduced sensitivity of oxygen in GD-MS and high oxygen background signal intensity as well as its variations. The absolute sensitivity procedure is shown as a matrix-independent approach, which provides quantitative values consistent with those obtained by carrier gas hot extraction (CGHE). KW - Fast flow GD KW - GDMS KW - Calibration KW - Matrix independent calibration PY - 2019 DO - https://doi.org/10.1039/c9ja00023b SN - 1364-5544 SN - 0267-9477 VL - 34 IS - 6 SP - 1109 EP - 1125 PB - Royal Society of Chemistry CY - London AN - OPUS4-47842 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique A1 - You, Zengchao A1 - Richter, Silke A1 - Benner, Philipp A1 - Recknagel, Sebastian T1 - The use of reference materials to improve the calibration strategy in glow discharge optical emission spectroscopy with machine learning N2 - 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. T2 - Winter Conference on Plasma Chemistry 2022 CY - Tucson, AZ, USA DA - 17.01.2022 KW - GDOES KW - Machine learning KW - Reference materials KW - Calibration KW - Cooper PY - 2022 AN - OPUS4-56497 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -