Expert System for the Evaluation of Measurement Uncertainty: Making Use of the Software Tool uncertaintyMANAGER®

An expert system for the evaluation of the measurement uncertainty is presented. It follows the four-step process established by the Eurachem/CITAC guide QUAM. The expert system provides a considerably better estimate of the overall measurement uncertainty than certain summary approaches used nowadays in most private and public laboratories and in industry. This is demonstrated by an example from the production control in a pharmaceutical lab. In addition, the expert system allows performing the entire process to evaluate the measurement uncertainty much faster than the summary approaches used in industry

Repeatability: some aspects concerning the evaluation of the measurement uncertainty

Various publications stress the importance of the repeatability (i.e. precision) of the calculation of the measurement of uncertainty. We reveal by detailing an example from production control in the pharmaceutical industry that the effect of other influence quantities should not be neglected, because their magnitude is even larger than the contribution of repeatability. We review the role of repeatability within the calculation of measurement uncertainty for several common validation and day-to-day measurement scenarios. They show that measurement models need to consider the measurement sequences of the various scenarios. Otherwise the size and effect of the repeatability might be overestimated. At the end Monte Carlo simulations were used to investigate the determination of the repeatability under certain restrictions. The simulation uncovered a significant bias toward the common formula for calculating the standard deviation when it is based on a duplicated measurement of a sampl

Characterizing the Urban Mine—Challenges of Simplified Chemical Analysis of Anthropogenic Mineral Residues

Anthropogenic mineral residues are characterized by their material complexity and heterogeneity, which pose challenges to the chemical analysis of multiple elements. However, creating an urban mine knowledge database requires data using affordable and simple chemical analysis methods, providing accurate and valid results. In this study, we assess the applicability of simplified multi-element chemical analysis methods for two anthropogenic mineral waste matrices: (1) lithium-ion battery ash that was obtained from thermal pre-treatment and (2) rare earth elements (REE)-bearing iron-apatite ore from a Swedish tailing dam. For both samples, simplified methods comprising ‘inhouse’ wet-chemical analysis and energy-dispersive Xray fluorescence (ED-XRF) spectrometry were compared to the results of the developed matrix-specific validated methods. Simplified wet-chemical analyses showed significant differences when compared to the validated method, despite proven internal quality assurance, such as verification of sample homogeneity, precision, and accuracy. Matrix-specific problems, such as incomplete digestion and overlapping spectra due to similar spectral lines (ICP-OES) or element masses (ICP-MS), can result in quadruple overestimations or underestimation by half when compared to the reference value. ED-XRF analysis proved to be applicable as semi-quantitative analysis for elements with mass fractions higher than 1000 ppm and an atomic number between Z 12 and Z 50. For elements with low mass fractions, ED-XRF analysis performed poorly and showed deviations of up to 90 times the validated value. Concerning all the results, we conclude that the characterization of anthropogenic mineral residues is prone to matrix-specific interferences, which have to be addressed with additional quality assurance measures.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität BerlinEC/H2020/641999/EU/ Prospecting Secondary raw materials in the Urban mine and Mining waste/ProSU

Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE

Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.EC/H2020/641999/EU/Prospecting Secondary raw materials in the Urban mine and Mining waste/ProSUMTU Berlin, Open-Access-Mittel – 202

WEEE Batteries - Sampling data

The attached data include the unit weight of more than 6000 waste electrical and electronic equipments (WEEE) and the batteries they contain. WEEE is classified according to the UNU keys (see Baldé, C.P.; Kuehr, R.; Blumenthal, K.; Gill, S.F.; Kern, M.; Micheli, P.; Magpantay, E.; Huisman, J.: E-waste statistics. Guidelines on classification, reporting, and indicators.). The WEEE batteries were divided into battery keys: LiPrim, LiRecharge, NiMH, NiCd, Pb, Zn, other, and unspecified.EC/H2020/641999/EU/Prospecting Secondary raw materials in the Urban mine and Mining waste/ProSU

Larger or More? Nanoparticle characterisation methods for recognition of dimers

Our article dissects the problem of understanding the origin of size heterogeneity in polydispersed nanoparticle samples. A commercially available multimodal material representing a typical borderline case of the nano definition is characterised with various state of the art techniques. Performance of the methods like dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), online coupled asymmetric field flow fractionation - multi angle light scattering (MALS) – DLS (FFF-MALS-DLS), tunable resistive pulse sensing (TRPS), centrifugal liquid sedimentation (CLS), analytical ultracentrifugation (AUC) and transmission electron microscopy (TEM) is discussed focusing on dimer (multimer) recognition capability of the single and some combined analytical solutions. NTA, TRPS and FFF-MALS are shown to resolve the multimodal size distribution of the sample, while batch mode DLS, the most widespread tool in characterisation laboratories, fails. Besides of complex methods like TEM imaging after FFF separation and FFF-MALS-DLS in combination with adequate mathematical shape factor models, centrifugal methods are documented as simple analytical tools that are able to indicate the presence of dimers made of rigid spherical nanoparticles.JRC.F.2-Consumer Products Safet