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Eingeladener Vortrag
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Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
The major source of the anthropogenic platinum group element (PGE) emission is attributed to the use of catalytic converters in automobiles. This paper describes the work performed by three National Metrology Institutes (Laboratoire national de métrologie et dessais, by the Physikalisch-technische bundesanstalt, Bundesanatalt für materialforschung und prûfung), in the framework of the Joint Research Project 'PartEmission' under the European Metrology Research Program. An analytical procedure based on a cationic exchange protocol and the isotope dilution or standard addition using an Inductived Coupled Plasma Mass Spectrometer, ICP-MS, for the quantification of the elements Pt, Pd and Rh from automotive exhaust emissions is described. Results obtained on a road dust certified reference (BCR 723) material showed a good agreement with the certified values, at ng/g levels, and relative expanded uncertainties within the range of 7–10%. Analysis of filters impacted with automotive exhaust particle emissions (from a diesel engine) showed the amount of collected PGE at levels of 10–1000 pg/filter. Their quantifications followed the developed analytical protocol that had been carried out with relative expanded uncertainties in the range of a few per cent up to 20% per filter. Nevertheless, a lack of homogeneity between the filters was observed, making the comparison between the project partners difficult in the sake of the validation of their analytical procedures on real samples.
A two-step separation procedure for the quantification of Pd and Pt in automotive exhaust emissions using isotope dilution mass spectrometry was established using a combination of cation and anion exchange chemistry. AG 50W-X12 was used as cation exchange resin and DGA as weakly basic anion exchange resin. This procedure enabled the effective separation of Pd and Pt from the matrix and from interfering elements. Additionally Pd and Pt were collected in separate chromatographic fractions, which increased the precision of the isotope ratio determination by separate measurements using single collector sector field ICPMS. The analytical procedure was validated by analysing the synthetically prepared samples and the certified reference materials BCR-723 (road dust) and IAEA-450 (algae). For the SI-traceable results complete uncertainty budgets were calculated yielding relatively expanded uncertainties (k = 2) of ≈1% for analyte masses in the ng range. Procedure blanks of 55 pg Pd and 3 pg Pt were obtained. The detection limits were calculated as 12 pg for Pd and 7 pg for Pt. Additionally, Pd and Pt blank levels of different filter materials are presented as well as the first results for automotive exhaust particles collected on cellulose filters.
Aluminum has gathered toxicological Attention based on relevant human exposure and its suspected hazardous potential. Nanoparticles from food supplements or Food contact materials may reach the human gastrointestinal tract.
Here, we monitored the physicochemical fate of aluminum containing nanoparticles and aluminum ions when passaging an in vitro model of the human gastrointestinal tract. Smallangle X-ray scattering (SAXS), transmission electron microscopy (TEM), ion beam microscopy (IBM), secondary ion beam mass spectrometry (TOF-SIMS), and inductively coupled plasma mass spectrometry (ICP-MS) in the singleparticle mode were employed to characterize two aluminumcontaining nanomaterials with different particle core materials (Al0, γAl2O3) and soluble AlCl3. Particle size and shape remained unchanged in saliva, whereas strong Agglomeration of both aluminum nanoparticle species was observed at low pH in gastric fluid together with an increased ion release. The levels of free aluminum ions decreased in intestinal fluid and the particles deagglomerated, thus liberating primary particles again. Dissolution of nanoparticles was limited and substantial changes of their shape and size were not detected. The amounts of particle-associated phosphorus, chlorine, potassium, and calcium increased in intestinal fluid, as compared to nanoparticles in standard dispersion.
Interestingly, nanoparticles were found in the intestinal fluid after addition of ionic aluminum. We provide a comprehensive characterization of the fate of aluminum nanoparticles in simulated gastrointestinal fluids, demonstrating that orally ingested nanoparticles probably reach the intestinal epithelium. The balance between dissolution and de novo complex formation should be considered when evaluating nanotoxicological experiments.
The application of appropriate analytical techniques is essential for nanomaterial (NM) characterization. In this study, we compared different analytical techniques for NM analysis. Regarding possible adverse health effects, ionic and particulate NM effects have to be taken into account. As NMs behave quite differently in physiological media, special attention was paid to techniques which are able to determine the biosolubility and complexation behavior of NMs. Representative NMs of similar size were selected: aluminum (Al0) and aluminum oxide (Al2O3), to compare the behavior of metal and metal oxides. In addition, titanium dioxide (TiO2) was investigated. Characterization techniques such as dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) were evaluated with respect to their suitability for fast characterization of nanoparticle dispersions regarding a particle's hydrodynamic diameter and size distribution. By application of inductively coupled plasma mass spectrometry in the single particle mode (SP-ICP-MS), individual nanoparticles were quantified and characterized regarding their size. SP-ICP-MS measurements were correlated with the information gained using other characterization techniques, i.e. transmission electron microscopy (TEM) and small angle X-ray scattering (SAXS). The particle surface as an important descriptor of NMs was analyzed by X-ray diffraction (XRD). NM impurities and their co-localization with biomolecules were determined by ion beam microscopy (IBM) and confocal Raman microscopy (CRM). We conclude advantages and disadvantages of the different techniques applied and suggest options for their complementation. Thus, this paper may serve as a practical guide to particle characterization techniques.
Uptake and molecular impact of aluminum-containing nanomaterials on human intestinal caco-2 cells
(2018)
Aluminum (Al) is one of the most common elements in the earth crust and increasingly used in food, consumer products and packaging. Its hazard potential for humans is still not completely understood. Besides the metallic form, Al also exists as mineral, including the insoluble oxide, and in soluble ionic forms. Representatives of these three species, namely a metallic and an oxidic species of Al-containing nanoparticles and soluble aluminum chloride, were applied to human intestinal cell lines as models for the intestinal barrier. We characterized physicochemical particle parameters, protein corona composition, ion release and cellular uptake. Different in vitro assays were performed to determine potential effects and molecular modes of Action related to the individual chemical species. For a deeper insight into signaling processes, microarray transcriptome analyses followed by bioinformatic data analysis were employed. The particulate Al species showed different solubility in biological media. Metallic Al nanoparticles released more ions than Al2O3 nanoparticles, while AlCl3 showed a mixture of dissolved and agglomerated particulate entities in biological media. The protein corona composition differed between both nanoparticle species. Cellular uptake, investigated in transwell experiments, occurred predominantly in particulate form, whereas ionic Al was not taken up by intestinal cell lines. Transcellular transport was not observed. None of the Al species showed cytotoxic effects up to 200 mg Al/mL. The transcriptome analysis indicated mainly effects on oxidative stress pathways, xenobiotic metabolism and metal homeostasis. We have shown for the first time that intestinal cellular uptake of Al occurs preferably in the particle form, while toxicological effects appear to be ion-related.
The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g.
Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content.
The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%.
Die Entwicklung innovativer Medium-Resolution-NMR-Spektrometer eröffnet eine Reihe bemerkenswerter Applikationen für die Qualitätskontrolle, die akademische Ausbildung und die Prozessanalytik. Die Verwendung von Permanentmagneten erlaubt den Einsatz der NMR-Spektroskopie in industriellen Prozessumgebungen.