TY - JOUR A1 - Gentzmann, Marie A1 - Paul, Andrea A1 - Serrano, Juan A1 - Adam, Christian T1 - Understanding scandium leaching from bauxite residues of different geological backgrounds using statistical design of experiments JF - Journal of Geochemical Exploration N2 - The leaching behavior of scandium (Sc) from bauxite residues can differ significantly when residues of different geological backgrounds are compared. The mineralogy of the source rock and the physicochemical environment during bauxitization affect the association of Sc in the bauxite i.e., how Sc is distributed amongst different mineral phases and whether it is incorporated in and/or adsorbed onto those phases. The Sc association in the bauxite is in turn crucial for the resulting Sc association in the bauxite residue. In this study systematic leaching experiments were performed on three different bauxite residues using a statistical design of experiments approach. The three bauxite residues compared originated from processing of lateritic and karstic bauxites from Germany, Hungary, and Russia. The recovery of Sc and Fe was determined by ICP-OES measurements. Mineralogical changes were analyzed by X-ray-diffraction and subsequent Rietveld refinement. The effects of various parameters including temperature, acid type, acid concentration, liquid-to-solid ratio and residence time were studied. A response surface model was calculated for the selected case of citric acid leaching of Hungarian bauxite residue. The investigations showed that the type of bauxite residue has a strong influence. The easily leachable fraction of Sc can vary considerably between the types, reaching ~20–25% in German Bauxite residue and ~50% in Russian bauxite residue. Mineralogical investigations revealed that a major part of this fraction was released from secondary phases such as cancrinite and katoite formed during Bayer processing of the bauxite. The effect of temperature on Sc and Fe recovery is strong especially when citric acid is used. Based on the exponential relationship between temperature and Fe-recovery it was found to be particularly important for the selectivity of Sc over Fe. Optimization of the model for a maximum Sc recovery combined with a minimum Fe recovery yielded results of ~28% Sc recovery at <2% Fe recovery at a temperature of 60 ◦C, a citric acid normality of 1.8, and a liquid-to-solid ratio of 16 ml/g. Our study has shown that detailed knowledge about the Sc association and distribution in bauxite and bauxite residue is key to an efficient and selective leaching of Sc from bauxite residues. KW - Bauxite residue KW - Scandium KW - Leaching KW - Design of experiments KW - Red mud PY - 2022 DO - https://doi.org/10.1016/j.gexplo.2022.107041 SN - 0375-6742 VL - 2022 IS - 240 SP - 1 EP - 13 PB - Elsevier Science CY - Amsterdam AN - OPUS4-55531 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rühlmann, Madlen A1 - Büchele, Dominique A1 - Ostermann, Markus A1 - Bald, Ilko A1 - Schmid, Thomas T1 - Challenges in the quantification of nutrients in soils using laser-induced breakdown spectroscopy – A case study with calcium JF - Spectrochimica Acta Part B N2 - The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed. KW - Laser-induced breakdown spectroscopy (LIBS) KW - Soil KW - Multivariate data analysis KW - Principal component analysis (PCA) KW - Partial least squares regression (PLSR) PY - 2018 DO - https://doi.org/10.1016/j.sab.2018.05.003 VL - 146 SP - 115 EP - 121 PB - Elsevier B.V. AN - OPUS4-45070 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Büchele, Dominique A1 - Chao, Madlen A1 - Ostermann, Markus A1 - Leenen, M. A1 - Bald, Ilko T1 - Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF JF - Scientic Reports N2 - Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred. KW - XRF KW - Chemometrics KW - Soil KW - Agriculture KW - Multivariate PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498671 DO - https://doi.org/10.1038/s41598-019-53426-5 VL - 9 SP - 17588 PB - Nature AN - OPUS4-49867 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -