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Arsenic is a redox-active metalloid whose toxicity and mobility strongly depends on its oxidation state, with arsenite (As(III)) being more toxic and mobile than arsenate (As(V)). Humic substances (HS) are also redox-active and can potentially react with arsenic and change its redox state. In this study we show that semiquinone radicals produced during microbial or chemical reduction of a HS model quinone (AQDS, 9,10-anthraquinone-2,6-disulfonic acid) are strong oxidants. They oxidize arsenite to arsenate, thus decreasing As toxicity and mobility. This reaction depends strongly on pH with more arsenite (up to 67.3%) being oxidized at pH 11 compared to pH 7 (12.6% oxidation) and pH 3 (0.5% oxidation). In addition to As(III) oxidation by semiquinone radicals, hydroquinones that were also produced during quinone reduction reduced As(V) to As(III) at neutral and acidic pH values (less than 12%) but not at alkaline pH. In order to understand redox reactions between arsenite/arsenate and reduced/oxidized HS, we quantified the radical content in reduced quinone solutions and constructed Eh-pH diagrams that explain the observed redox reactions. The results from this study can be used to better predict the fate of arsenic in the environment and potentially explain the occurrence of oxidized As(V) in anoxic environments.
There is a high demand of monitoring in the era of QbD in industrial scale require new approaches to gain data rapidly and of sufficient quality in real time. Raman spectroscopy technology has great potential but not yet shown it fully in process on-line monitoring due to limitations such as i) uncomplete separation between cells and growth media alone, ii) general weak Raman signals of analytes in complex solutions and iii) strong background signals such as the auto-fluorescence, cosmic rays and surrounding lights overlapping the weak Raman signals. Here we demonstrate a Proof-of-Concept on an the example lactic acid bacteria process using a Streptococcus thermophiles fermentation. Results from three different Raman approaches are presented: 1) Time-Gated Raman Spectroscopy (TG-Raman), 2) Surface Enhanced Raman Spectroscopy (SERS) and 3) Raman process spectroscopy with NIR excitation combined with multivariate data analysis (MVDA) using Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR).
Reaction monitoring in disperse systems, such as emulsions, is of significant technical importance in various disciplines like biotechnological engineering, chemical industry, food science, and a growing number other technical fields. These systems pose several challenges when it comes to process analytics, such as heterogeneity of mixtures, changes in optical behavior, and low optical activity. Concerning this, online nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for process monitoring in complex reaction mixtures due to its unique direct comparison abilities, while at the same time being non-invasive and independent of optical properties of the sample. In this study the applicability of online-spectroscopic methods on the homogeneously catalyzed hydroformylation system of 1-dodecene to tridecanal is investigated, which is operated in a mini-plant scale at Technische Universität Berlin. The design of a laboratory setup for process-like calibration experiments is presented, including a 500 MHz online NMR spectrometer, a benchtop NMR device with 43 MHz proton frequency as well as two Raman probes and a flow cell assembly for an ultraviolet and visible light (UV/VIS) spectrometer. Results of high-resolution online NMR spectroscopy are shown and technical as well as process-specific problems observed during the measurements are discussed.
The Cultivation of “Saccharomyces cerevisiae” for enzyme production was monitored using Near-infrared spectroscopy. An inline NIR optrode was therefore immersed in a 15 L vessel. The calibration was done using a Partial Least Squares (PLS) model with reference measurements of glucose, ammonium, phosphate, ethanol, and optical density. A nonlinear biological process model based on an extended Kalman Filter (EKF) was used to describe the fermentation behavior. It was found that EKF corrects inaccurate PLS predictions.
Hydroformylation represents an important homogeneous catalyzed process, which is widely used within chemical industry. Usually applied with simple alkenes like Propene and Butene aldehydes obtained from alkenes >C6 are relevant intermediates in production of plasticizers, surfactants and polymers. Today the active catalyst species is often based on valuable Rhodium complexes in aqueous solution. This implies the problem of limited water solubility of the reactands, which is acceptable for short chain lengths, but states a problem in case of higher alkenes. Along with that efficient separation and recycling of the catalyst becomes more complicated. There are different approaches tackling this problem, e.g., by using of salt formation in the BASF process or downstream distillation within the Shell process
The Collaborative Research Center InPROMPT aims to establish a novel process concept for the hydroformylation of long-chained olefins, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro-emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the temperature and concentration sensitive multi-phase system demands a continuous observation of the reaction to achieve an operational and economically feasible plant operation. For that purpose, we tested the potential of both NMR and Raman spectroscopy for process control assistance. The lab-scale experiments were supported by sampling for off-line GC-analysis as reference analytics. The results of the NMR experiments will be part of another contribution.
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris, which leads to the accumulation of microscopic plastic particles of still unknown fate, is an upcoming problem of our time. In order to monitor the degree of contamination and to understand the underlying processes of degradation and internalization of plastic debris, analytical methods are urgently needed, which help to identify and quantify microplastics. Currently, expensive collected and purified materials enriched on filters are investigated by (micro) infrared spectroscopy (FTIR). Few studies using micro-Raman spectroscopy have been published as well. In contrast to FTIR, Raman spectroscopy can handle wet samples, but it suffers from interference of fluorescent materials. Both micro-FTIR- and micro-Raman, always include time consuming scanning and mapping procedures followed by the manual inspection and measurement of selected particles.
The use of magnetic materials as heterogeneous catalysts has attracted increasing attention in the last years since they proved to be promising candidates for water treatment. In the present study, two types of surface-modified magnetite (Fe3O4) nanoparticles, coated with non-hazardous naturally occurring agents—either tannic acid (TA) or dissolved natural organic matter—were evaluated as magnetic heterogeneous catalysts. Chemical synthesis (co-precipitation) was chosen to yield the nanocatalysts due to its well-established simplicity and efficiency. Subsequently, the properties of the final products were fully assessed by various characterization techniques. The catalytic activity in heterogeneous oxidation of aqueous solutions containing a model pollutant, Bisphenol A (BPA), was comparatively studied. The effect of operational parameters (catalyst loading, H2O2 dosage, and UV light irradiation) on the Degradation performance of the oxidation process was investigated. The optimum experimental parameters were found to be 1.0 g/L of catalysts and 10 mM H2O2, under UV irradiation. The highest mineralization rates were observed for Fe3O4-TA catalyst. More than 80 % of BPA was removed after 30 min of reaction time under the specified experimental conditions. The obtained results showed that the two catalysts studied here are suitable candidates for the removal of pollutants in wastewaters by means of heterogeneous reaction using a green sustainable treatment method.
A major industrial reaction based on homogeneous catalysis is hydroformylation for the production of aldehydes from alkenes and syngas. Hydroformylation in microemulsions, which is currently under investigation at Technische Universität Berlin on a mini-plant scale, was identified as a cost efficient approach which also enhances product selectivity. Herein, we present the application of online Raman spectroscopy on the reaction of 1-dodecene to 1-tridecanal within a microemulsion. To achieve a good representation of the operation range in the mini-plant with regard to concentrations of the reactants a design of experiments was used. Based on initial Raman spectra partial least squares regression (PLSR) models were calibrated for the prediction of 1-dodecene and 1-tridecanal. Limits of predictions arise from nonlinear correlations between Raman intensity and mass fractions of compounds in the microemulsion system. Furthermore, the prediction power of PLSR models becomes limited due to unexpected by-product formation. Application of the lab-scale derived calibration spectra and PLSR models on online spectra from a mini-plant operation yielded promising estimations of 1-tridecanal and acceptable predictions of 1-dodecene mass fractions suggesting Raman spectroscopy as a suitable technique for process analytics in microemulsions.
Due to the increasing presence of microplastic particles (MP) in the Environment and the unknown risks arising from them, there is an urgent need for analytical methods that allow for an efficient identification and quantification of microplastics (MP), i.e. particles < 5 mm, in environmental samples. So far, mostly timeconsuming (micro) infrared or micro-Raman spectroscopic methods are applied. Here an faster alternative Approach is presented based on a Raman processspectrometer with fiber-optical probes in combination with multivariate data analysis.
Nitroaromaten und insbesondere Trinitrotoluol (TNT) sind weit verbreitete Spreng- und Umweltschadstoffe. Die größte Herausforderung bei der Detektion von TNT in der Gasphase ist der geringe Dampfdruck. Derzeit werden vielerorts günstige, schnelle, handliche und einfach zu bedienende Alternativen zur klassischen TNT-Analytik entwickelt. Aktuell existieren keine einheitlichen Richtlinien für Sprengstoffsensoren. Hier wird die Entwicklung eines Messplatzes zur Validierung von Sprengstoffsensoren sowie die Erprobung eines kompakten Mustersensors vorgestellt.
Medium resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra were treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprised direct integration, automated line fitting, indirect hard modeling, and partial least squares regression.
The project InPROMPT aims to establish a robust hydroformylation reaction process of the alkene n-dodecene to the aldehyde n-tridecanal using a rhodium complex as catalyst in the presence of syngas. This industrial relevant production process demands further research for optimization and expansion. For that purpose, the fiber optic coupled probe of a process Raman spectrometer is directly introduced in the product stream, which enables the recording of spectra within short control intervals.
Zur Onlineüberwachung von Fermentationsprozessen werden zunehmend schwingungsspektroskopische (Raman-, Mittel- und Nahinfrarotspektroskopie (NIRS) und UV-/VIS- Methoden(Absorptions- und Fluoreszenzspektroskopie) in Kombination mit multivariater Auswertung eingesetzt. Anliegen dieser Arbeit war es, zu testen, welche Verfahren für die spektroskopische On- und Offlineüberwachung des Fermentationsprozesses zur biotechnologischen Herstellung einer Malariavaccine grundlegend geeignet sind und welche Messbereiche und Genauigkeiten der Vorhersage im Idealfall bei der Abwesenheit von Hefezellen erzielt werden können.
With a more and more general acceptance of accreditation in the field of reference materials production, and an ever increasing number of RMP accredited, accreditation bodies face applications from RMP active in the field of qualitative RM production.
While (the still valid and used for accreditation purposes) ISO Guides 34 and 35 describe in detail requirements applicable to RMP dealing with quantitative RM, accreditation bodies normally claim lack of normative requirements with respect to quantitative RM. The new ISO 17034:2016 which will be introduced in the accreditation practice over the next two to three years solves the problem pragmatically, allowing strategies for RM certification other than those for (purely) quantitative materials.
In fact, both ISO Guide 34 and the new ISO 17034 are written in a form that, at least for the overwhelming majority of requirements, may be applied to RMP of both qualitative and quantitative RM. Peculiarities may occur in homogeneity and stability testing, and the uncertainty of a purely qualitative result is still under discussion. The problem of traceability might be solved for most of the application fields considered.
The talk gives an overview of the specific problems encountered, and provides some possible solutions both for homogeneity and stability testing, the expression of uncertainty, and the statement of traceability in certificates.
Qualitative reference materials (RM) cover a wide range of the overall RM market. Proficiency testing providers attract up to a thousand of participants in PT schemes purely oriented on qualitative results. The RM used for these kinds of PT are poorly regulated, nevertheless with a more and more general acceptance of accreditation in the field of RM production and PT provision, there is an ever increasing interest in assessing producers and providers according to rules already well accepted in the field of quantitative analysis.
The basic governing document, ISO 17034:2016, is written in a form that, at least for the overwhelming majority of requirements, may be applied to both qualitative and quantitative RM. However, problems remain. In particular, the expression of uncertainty of a purely qualitative result is still unresolved, and under discussion, the latter now lasting already dozens of years.
Some handles would be needed. In the poster, existing approaches and some pragmatic, new ways to tackle the problem are displayed and discussed.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process Monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
The growing need to implement sensors such as NIR or Raman spectroscopy for the in-situ monitoring of bioprocesses which follows the standards of Quality by Design is either restricted by the impact of the huge water signal or by a disturbing fluorescence background originating from compounds in the culture media. Furthermore, the characterization of the bioprocess samples is challenging due to changing conditions in course of cultivation.
Here we evaluate two different process-suitable Raman spectroscopic approaches, namely time-gated Raman which bears the potential to extract the Raman signal from the fluorescence background, and cw- Raman with NIR excitation in combination with Surface Enhanced Raman Spectroscopy- (SERS) to investigate cell-free supernatants of Escherichia coli sampled over the course of a cultivation. A confocal Raman microscope was used as a reference for the process devices. The concentration of the analytes, glucose, acetate as well as metabolites such as cAMP, AMP and amino-acids were determined by offline by High-Performance Liquid Chromatography (HPLC) to serve as reference for the calibration of the Raman and SERS spectral data.
Multivariate evaluation of the Raman and SERS spectra by Partial Least Squares Regression (PLSR) yielded for most of the analytes robust correlations at each sampling point. Repeated investigation of the off-line samples over a larger experimental period suggested not only a high reliability of the Raman data in general but also a high repeatability of the SERS experiments. Similar spectral features in different quality and signal/noise ratios were measured with all three set-ups. Major results of the comparison of the different Raman spectroscopic approaches and their combination with SERS are summarized and conclusions are drawn on which approach provides the most accurate concentration data among the target analytes.
Acknowledgement
The authors kindly thank Mario Birkholz (IHP, Frankfurt (Oder), Germany) for the opportunity to use a confocal Raman microscope, Alex Bunker and Tapani Viitala (Division of Pharmaceutical Biosciences, Centre for Drug Research, University of Helsinki, Finland).
Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.
Spectroscopic methods possess important advantages for the observation of bioprocesses: they are robust, gain many data within a short time scale, and allow for the measurement of multiple relevant information at the same time. Next to Near-infrared spectroscopy, Raman spectroscopy is increasingly applied as a powerful online tool to monitor bioprocesses. However, Raman spectra may be obscured by straylight, cosmic rays and fluorescence. To achieve the best performance, optimal conditions for spectral quality and stability should be pursued. This comprises not only the choice of highly-sensitive equipment, but also the way of implementation into the process. Here we compared the potential of introducing a sterilized fiber optical Raman probe, and a flow-through cell into the bypass of a stirred tank reactor to monitor an autotrophic cultivation of Ralstonia eutropha where gasses are the energy and carbon sources. Raman spectra were continuously recorded over seven days and 22 samples were taken for the reference analytics of biomass, optical density, and protein. In addition, data for of phosphate and ammonium were available. The quantitative Evaluation of Raman spectra by Partial Least Squares Regression (PLSR) yielded comparable results for both approaches, however, there was a general trend of lower prediction errors with the immersion probe.
The better performance of the non-contact optic may be either related to a more representative interaction of the measurement optics with the probe or simply to the absence of probe-fouling, which was observed with the immersion optic in the interstice between window and metal frame.
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, here we tested a macroscopic dimensioned NIR process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 µm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils and real-world samples, e.g. and fermenter residue, suggest a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pre-treatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.
A process spectroscopic approach for the monitoring of microplastics in environmental samples
(2018)
The potential of Raman and NIR process-spectroscopic as a rapid approach for the estimation of microplastics (MP) in soil matrix were tested. For this purpose, artificial MP/soil mixture samples consisting of standard soils or sand with defined ratios of MP (0,5 – 10 mass% polymer) from polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET) were prepared. MP particles with diameters < 2 mm and < 125 µm were obtained from industrial polymer pellets after cryo-milling. Spectra of these mixtures were collected by (i) a process FT-NIR spectrometer equipped with a reflection probe, (ii) by a cw-process Raman spectrometer and (iii) by a time-gated Raman spectrometer using fiber-optic probes. The evaluation of process-spectra was performed by chemometric methods. Whereas MP could be detected by Raman spectroscopy in coastal sand at 0.5 mass%, in standard soil detection of MP was limited to 10 – 5 mass% with the large fraction, but samples containing particles of the 125µm mass-fraction yielded no positive result at all. One reason for the lacking sensitivity could be fluorescence by soil organic matter and thus, in a next test time-gated Raman spectroscopy was applied. However, although being indeed more sensitive to the small particles this method failed at MP < 5 mass% indicating that fluorescence was not the major problem. Finally, FT-NIR was tested. Depending on the polymer, MP contents of 0,5 or 1 mass%, respectively, could be detected in standard soils and polymers identified. Furthermore, this approach could be used for the investigation of mixtures of up to four polymers and in real-world samples from bio-waste fermenter residues.
Immer im Bild mittels Prozess-Spektroskopie - Spektroskopische Methoden für die Prozessanalytik
(2012)
Hydrothermal carbonization (HTC) can be used for converting the biomass into a carbon-rich material, whose application as a fuel requires higher heating value, whereas soil amendment needs stable carbon. This work was focused on the characterization of hydrochars derived from microcrystalline cellulose. The chars were investigated using elemental analysis, Brunauer–Emmett–Teller technique, nuclear magnetic resonance spectroscopy, Raman, Fourier transform infrared, and electron spin resonance spectroscopy. Severity in temperature between 230 and 270 °C with reaction times between 2 and 10 h only affect the carbon content moderately. The results show that aromatization of HTC chars correlates well with temperature, which was further supported by the increase of organic radicals with decreasing g values at higher temperatures. Based on these results, the energetic use of chars favors mild HTC (T < 230 °C and t ≤ 6 h), while the soil amendement favors serve conditions (T ≥ 230 °C, and t > 6 h).
The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIRprocess Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis.
Ziele und Durchführung eines geplanten Ringversuchs zur Untersuchung eines Dreikomponentensystems mittels NIR werden vorgestellt. Der Vortrag enthält einen kurzen Rückblick auf einen 2014 durchgeführten Ringversuch, wobei ein flüssiges Zweikomponentensystem untersucht wurde. Im Anschluss erfolgt die Erläuterung der verwendeten Komponenten, es werden Teilnahmebedingungen und mögliche Zeiträume bekannt gegeben.
More than 80 representatives of SMEs, industrial companies and research institutes met on September 12 at the workshop "Challenges in Additive Manufacturing: Innovative Materials and Quality Control" at BAM in Adlershof to discuss the latest developments in materials and quality control in additive manufacturing.
In special lectures, researchers, users and equipment manufacturers reported on the latest and future developments in additive manufacturing. Furthermore, funding opportunities for projects between SMEs and research institutions on a national and European level were presented.
Im DFG-Schwerpunktprogramm 1374, Teilprojekt FunWood IV, erfolgt in Kooperation mit dem Institut für Bioanalytik an Hochschule für angewandte Wissenschaften, Coburg und dem Helmholtz-Zentrum für Umweltforschung (UFZ), Leipzig die Untersuchung des Ligningehalts von natürlich gealterten Hölzern. Zu diesem Zweck soll an der BAM eine NIR spektroskopische Methode entwickelt werden, die auch zur Untersuchung großer Stückzahlen geeignet ist. Insgesamt liegen exponierte Proben von 13 verschiedenen Hölzern vor, die sukzessive durch die Kooperationspartner kryovermahlen und getrocknet werden. Für jede der 13 Arten wurden jeweils 300 Holzstücken ausgebracht, exponiert und im Sommer 2018 wieder eingesammelt. Die Ergebnisse sollen als Grundlage für die Bewertung eines Langzeitversuchs zum Einfluss von Waldbewirtschaftungsintensität und Baumartenidentität über mehrere geographische Dimensionen dienen.
Modular plants using intensified continuous processes represent an appealing concept to produce pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes, and it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and includes a compact Nuclear Magnetic Resonance (NMR) spectrometer for online quality monitoring as well as a new model-based control approach. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including ro-bust evaluation of sensor data.
Here, we present alternatives for the quantitative determination of the analytes using modular, physically motivated models. These models can be adapted to new substances solely by the use of their corresponding pure component spectra, which can either be derived from experimental spectra as well as from quantum mechanical models or NMR predictors. Modular means that spec-tral models can simply be exchanged together with alternate reagents and products. Beyond that, we comprehensively calibrated an NIR spectrometer based on online NMR process data for the first time within an industrial plant. The integrated solution was developed for a metal organic reac-tion running on a commercial-scale modular pilot plant and it was tested under industrial conditions.
Die Untersuchungen sind durch das das DFG Schwerpunktprogramm 1374 für Biodiversitätsforschung motiviert. Anhand des überwachten Abbaus von Totholz soll untersucht werden, welche Auswirkung unterschiedliche Formen und Intensitäten der Landnutzung auf die Biodiversität innerhalb von drei Exploratorien zeigen. Bei den vorangegangenen Studien wurden Nahrungs¬netzte auf organismischer Ebene sowie Klima- und Umgebungsdaten untersucht und die Veränderung des ausgebrachten Totholzes als Masseverlust erfasst. Es wird jedoch erwartet, dass in Anhängigkeit der beteiligten Organismen und Umgebungsparameter verschiedene Mechanismen des Holzabbaus vorliegen, die in unterschiedlichen Abbauraten der Holzkomponenten Lignin, Cellulose und Hemicellulose resultieren. Aus diesem Grund ist die quantitative Untersuchung des Ligningehalts an Hölzern ein wesentlicher Schritt für ein verbessertes Verständnis der Biodiversität in den untersuchten Exploratorien. Um der hohen Probenanzahl, die sich aus der Kontrolle von jeweils 100 Proben (Plots) in den drei Exploratorien für Hölzer aus 13 Baumarten ergibt, gerecht zu werden, wird eine schnelle und effiziente Untersuchungsmethode benötigt. Diese wird derzeit in Form einer NIR-spektroskopischen Bestimmung des Ligningehalts entwickelt. Um basierend auf den NIR Spektren vermahlener Holzproben Lignin und weitere Holzkomponenten korrekt vorherzusagen, muss zunächst basierend auf nasschemischen Analysen ein chemometrisches Modell kalibriert werden. Dazu ist die Charakterisierung von mindestens 20 – 30 Proben, die eine hohe Varianz des Ligningehalts aufweisen, notwendig. Im Vortrag werden erste Untersuchungs¬ergebnisse für die Vorhersage des Ligningehalts von Kiefernhölzern, die für ein Jahr in dem Exploratorium Schorfheide exponiert wurden, vorgestellt. Die anschließende explorative Datenanalyse (PCA) unter Verwendung von insgesamt 28 Variablen zur Beschreibung klimatischer und Umgebungsparameter weist eine Reihe interessanter Korrelationen auf. Demnach ist der Ligningehalt auf der ersten Hauptkomponente (29% erklärte Varianz) positiv mit dem C/N-Verhältnis und negativ mit Landnutzungsindex korreliert. Masseverlust und Temperatur hingegen dominieren die zweite Hauptkomponente (12% erklärte Varianz).
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.
In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.
In the following the contents of the files are explained.
The transformation of a base-catalyzed, mechano-assisted Knoevenagel condensation of mono-fluorinated benzaldehyde derivatives (p-, m-, o-benzaldehyde) with malonodinitrile was investigated in situ and in real time. Upon milling, the para-substituted product was found to crystallize initially into two different polymorphic forms, depending on the quantity of catalyst used. For low catalyst concentrations, a mechanically metastable phase (monoclinic) was initially formed, converting to the mechanically stable phase (triclinic) upon further grinding. Instead, higher catalyst concentrations crystallize directly as the triclinic product. Inclusion of catalyst in the final product, as evidenced by mass spectrometric analysis, suggests this complex polymorphic pathway may be due to seeding effects. Multivariate analysis for the in situ Raman spectra supports this complex formation pathway, and offers a new approach to monitoring multi-phase reactions during ball milling.
Within the Collaborative Research Center InPROMPT a novel process concept for the hydroformylation of long-chained olefins is studied in a mini-plant, using a rhodium complex as catalyst in the presence of syngas. Recently, the hydroformylation in micro¬emulsions, which allows for the efficient recycling of the expensive rhodium catalyst, was found to be feasible. However, the high sensitivity of this multi-phase system with regard to changes in temperature and composition demands a continuous observation of the reaction to achieve a reliable and economic plant operation. For that purpose, we tested the potential of both online NMR and Raman spectroscopy for process control. The lab-scale experiments were supported by off-line GC-analysis as a reference method.
A fiber optic coupled probe of a process Raman spectrometer was directly integrated into the reactor. 25 mixtures with varying concentrations of olefin (1-dodecene), product (n-tridecanal), water, n-dodecane, and technical surfactant (Marlipal 24/70) were prepared according to a D-optimal design. Online NMR spectroscopy was implemented by using a flow probe equipped with 1/16” PFA tubing serving as a flow cell. This was hyphenated to the reactor within a thermostated bypass to maintain process conditions in the transfer lines.
Partial least squares regression (PLSR) models were established based on the initial spectra after activation of the reaction with syngas for the prediction of unknown concentrations of 1-dodecene and n-tridecanal over the course of the reaction in the lab-scale system. The obtained Raman spectra do not only contain information on the chemical composition but are further affected by the emulsion properties of the mixtures, which depend on the phase state and the type of micelles. Based on the spectral signature of both Raman and NMR spectra, it could be deduced that especially in reaction mixtures with high 1-dodecene content the formation of isomers as a competitive reaction was dominating. Similar trends were also observed during some of the process runs in the mini-plant. The multivariate calibration allowed for the estimation of reactants and products of the hydroformylation reaction in both laboratory setup and mini-plant.
Monitoring chemical reactions is the key to chemical process control. Today, mainly
optical online methods are applied. NMR spectroscopy has a high potential for direct
loop process control. Compact NMR instruments based on permanent magnets
are robust and relatively inexpensive analysers, which feature advantages like low
cost, low maintenance, ease of use, and cryogen-free operation. Instruments for
online NMR measurements equipped with a flow-through cell, possessing a good
signal-to-noise-ratio, sufficient robustness, and meeting the requirements for
integration into industrial plants (i.e., explosion safety and fully automated data
analysis) are currently not available off the rack.
Intensified continuous processes are in focus of current research. Flexible (modular)
chemical plants can produce different products using the same equipment with short
down-times between campaigns and quick introduction of new products to the
market. In continuous flow processes online sensor data and tight closed-loop control
of the product quality are mandatory. If these are not available, there is a huge risk of
producing large amounts of out-of-spec (OOS) products. This is addressed in the
European Unionʼs Research Project CONSENS (Integrated Control and Sensing)
by development and integration of smart sensor modules for process monitoring and
control within such modular plant setups.
The presented NMR module is provided in an explosion proof housing of 57 x 57 x
85 cm module size and involves a compact 43.5 MHz NMR spectrometer together
with an acquisition unit and a programmable logic controller for automated data
preparation (phasing, baseline correction) and evaluation. Indirect Hard Modeling
(IHM) was selected for data analysis of the low-field NMR spectra. A set-up for
monitoring continuous reactions in a thermostated 1/8” tubular reactor using
automated syringe pumps was used to validate the IHM models by using high-field
NMR spectroscopy as analytical reference method.
Hydroformylation of short-chained olefins has been established as a standard industrial process for the production of C2 to C6 aldehydes. Using aqueous solutions of transition metal complexes these processes are carried out homogeneously catalyzed. A biphasic approach allows for highly efficient catalyst recovery. Regarding renewable feedstocks, the hydroformylation of long-chained alkenes (> C10) in a biphasic system, using highly selective rhodium catalysts has yet not been shown. Therefore, the Collaborative Research Center SFB/TR 63 InPROMPT develops new process concepts, involving innovative tuneable solvent systems to enable rather difficult or so far nonviable synthesis paths. One possible concept is the hydroformylation of long-chained alkenes in microemulsions. For this, a modular mixer-settler concept was proposed, combining high reaction rates and efficient catalyst recycling via the application of technical grade surfactants. The feasibility of such a concept is evaluated in a fully automated, modular mini-plant system within which the characteristics of such a multiphase system pose several obstacles for the operation. Maintaining a stable phase separation for efficient product separation and catalyst recycling is complicated by small and highly dynamic operation windows as well as poor measurability of component concentrations in the liquid phases. In this contribution, a model-based strategy is presented to enable concentration tracking and phase state control within dynamic mini-plant experiments. Raman spectroscopy is used as an advanced process analytical tool, which allows for online in-situ tracking of concentrations. Combined with optical and conductivity analysis optimal plant trajectories can be calculated via the solution of dynamic optimization problem under uncertainty. Applying these, a stable reaction yield of 40 % was achieved, combined with an oil phase purity of 99,8 % (total amount of oily components in the oil phase) and catalyst leaching below 0.1 ppm.
A first pilot study on the sorption of environmental pollutants on various microplastic materials
(2017)
With the drastic increase in plastic production, the input of plastic particles into the environment has become a recognised problem.
Xenobiotics are able to sorb to polymer materials, and this process is further enhanced where they Encounter microplastics (plastic fragments <5 mm). In this work we studied the sorption of metformin, a type-2 diabetes drug, and difenoconazole, a fungicide, onto the virgin polymer materials polyamide (PA), polypropylene (PP), and polystyrene (PS). Additionally, PP was cryo-milled and PA was treated with acid to investigate the influence of an increase in surface area and chemical modification. The material properties were also studied by dynamic scanning calorimetry (DSC), gel permeation chromatography (GPC) and Fourier transform infrared spectroscopy (FTIR). Sorption experiments were performed on the basis of a full factorial design examining the effect of agitation, pH value, and salinity. Experimental results showed that difenoconazole sorbs readily to all microplastics, whereas the more polar analyte metformin did not show any affinity to the materials used. For difenoconazole the governing factor in all cases is agitation, while both pH and salinity exhibited only a slight influence. The modification of polymers leads to enhanced sorption, indicating that an increase in surface area (cryo-milled PP) or inner volume (acid-treated PA) strongly favours adsorption. Moreover, long-term experiments demonstrated that the time until equilibrium is reached depends strongly on the particle size.
Green Fenton-like magnetic nanocatalysts: Synthesis, characterization and catalytic application
(2015)
Five iron oxalate core–shell magnetite nanoparticles catalysts are evaluated as magnetic heterogeneous materials. Simple and efficient procedures for the preparation of magnetic iron oxalate coated nanoparticles are presented. The catalysts are fully characterized using various investigation techniques. Additionally, the formation of photo-sensitized oxygen by spin-trapping using electron spin resonance is investigated. The catalytic activity of two model substrates (carbamazepine and bisphenol A) is also evaluated. The effect of operational parameters (catalyst and H2O2 concentration, UVA light) on the degradation performance of the oxidation process is investigated. The obtained reaction rates depend on the nature of the compound and increase with iron oxide shell thickness of the catalyst. Moreover, these materials show a significant activity during two consecutive tests. The optimum experimental parameters are found to be 1.0 g L−1 of catalysts, 10 mM H2O2, under UVA irradiation. More than 99% of both substrates are removed after 30 min of reaction time under the experimental conditions given above. The results obtained show that the catalysts are suitable candidates for the removal of pollutants in wastewaters by means of the Fenton heterogeneous reaction.
Two types of surface modified magnetite (Fe3O4) nanoparticles, coated with either tannic acid (TA) or dissolved natural organic matter (NOM), were evaluated as magnetic heterogeneous catalysts. Simple and efficient procedures for the synthesis of the magnetic catalysts were employed, their properties being fully assessed by various characterization techniques. The catalytic activity in heterogeneous oxidation of aqueous solutions containing Bisphenol A (BPA) over the catalysts was comparatively studied. The optimum experimental parameters were: 1g/L of catalysts, 10 mM H2O2, under UV irradiation. The highest mineralization rates were observed for Fe3O4-TA catalyst. More than 80% of BPA were removed after 30 minutes of reaction time under the specified experimental conditions. The results showed that the obtained catalysts are suitable candidates for the removal of pollutants in wastewaters by means of heterogeneous reaction
Green Fenton-like magnetic nanocatalysts: Synthesis, characterization and catalytic application
(2015)
Five iron oxalate core–shell magnetite nanoparticles catalysts are evaluated as magnetic heterogeneous materials. Simple and efficient procedures for the preparation of magnetic iron oxalate coated nanoparticles are presented. The catalysts are fully characterized using various investigation techniques. Additionally, the formation of photo-sensitized oxygen by spin-trapping using electron spin resonance is investigated. The catalytic activity of two model substrates (carbamazepine and bisphenol A) is also evaluated. The effect of operational parameters (catalyst and H2O2 concentration, UVA light) on the degradation performance of the oxidation process is investigated. The obtained reaction rates depend on the nature of the compound and increase with iron oxide shell thickness of the catalyst. Moreover, these materials show a significant activity during two consecutive tests. The optimum experimental parameters are found to be 1.0 g L-1 of catalysts, 10 mM H2O2, under UVA irradiation. More than 99% of both substrates are removed after 30 min of reaction time under the experimental conditions given above. The results obtained show that the catalysts are suitable candidates for the removal of pollutants in wastewaters by means of the Fenton heterogeneous reaction.
Two iron-based molten salts comprising an imidazolium and Schiff base were evaluated as catalysts for removal of carbamazepine (CBZ) from water. The catalysts were fully characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectrometry (EDX), nuclear magnetic resonance spectroscopy (NMR), electrospray ionisation–mass spectrometry (ESI–MS), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and nitrogen adsorption–desorption isotherms (BET). Additionally, the formation of photo-sensitized oxygen was investigated by spin-trapping using electron spin resonance (ESR). The catalytic activity in heterogeneous oxidation of the micropollutant (CBZ) was also evaluated. The effects of catalyst loading, pH, H2O2 dosage and UV light on the oxidation of the selected compound were investigated. After 15 min of UVA irradiation in the presence of 200 μM H2O2, CBZ was completely removed over both catalysts.
Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
UV-induced transformation of dissolved
organic matter (DOC) is often accompanied by reduction
of molecular weight and aromaticity and an increase of
low-molecular weight (LMW) matter that can be utilized as
a substrate by heterotrophic bacteria. Moreover, the generation
of reactive transients and mineralization of DOC
occurs. For a better understanding of the modification that
starts after irradiation and to distinguish between possible
chemical and biological modifications, we selected different
natural organic matter (NOM) from Norway and
Germany. The aqueous solutions were treated by UV
irradiation and divided into two aliquot samples. NaN3
anti-bacterial treatment was applied to one sample, and
high-pressure size-exclusion chromatography (HPSEC)
analysis was used for both. In all samples, we found typical
modifications of NOM after UV irradiation. Incubation
(>7 days) of UV-irradiated NOM samples resulted in
lower levels of LMW matter and increased aromaticity.
Parallel to these changes of carbon fractions, an increase in
bacterial cell numbers was observed. Addition of NaN3 to
NOM, however, inhibited the reduction of LMW matter,
indicating that microbial activity accounted for the
observed changes in NOM. Analysis of the bacterial
community composition by denaturing gradient gel electrophoresis
(DGGE) of the amplified 16S rRNA genes
revealed that bacterial communities of non-irradiated and
UV-irradiated NOM were different and that UV selected
for specific members of α-proteobacteria, β-proteobacteria,
and Bacteriodetes. Our results imply that after UV-irradiation
of NOM, specific bacterial members are well adapted
to low pH, high LMW DOC concentrations, and oxidative
stress, and therefore thrive well on UV-irradiated humic
matter.
The authenticity of objects and artifacts is often
the focus of forensic analytic chemistry. In document fraud
cases, the most important objective is to determine the
origin of a particular ink. Here, we introduce a new
approach which utilizes the combination of two analytical
methods, namely Raman spectroscopy and laser-induced
breakdown spectroscopy (LIBS). The methods provide
complementary information on both molecular and elemental
composition of samples. The potential of this hyphenation
of spectroscopic methods is demonstrated for ten blue
and black ink samples on white paper. LIBS and Raman
spectra from different inks were fused into a single data
matrix, and the number of different groups of inks was
determined through multivariate analysis, i.e., principal
component analysis, soft independent modelling of class
analogy, partial least-squares discriminant analysis, and
support vector machine. In all cases, the results obtained
with the combined LIBS and Raman spectra were found to
be superior to those obtained with the individual Raman or
LIBS data sets.
An alternative spectroscopic approach for the monitoring of microplastics in environmental samples
(2017)
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown fate. To monitor the degree of contamination and to understand the underlying processes of turnover, analytical methods are urgently needed, which help to identify and quantify microplastic (MP). Currently, costly collected and purified materials enriched on filters are investigated both by micro-infrared spectroscopy and micro-Raman. Although yielding precise results, these techniques are time consuming and restricted to sample aliquots in the order of micrograms precluding prompt and representative information on both, larger sample numbers and realistic material volumes. To overcome these problems, here we tested Raman and NIR process-spectroscopic methods in combination with multivariate data analysis.
For this purpose, artificial MP/soil mixture samples consisting of standard soils or sand with defined ratios of MP (0,5 – 10 mass% polymer) from polyethylene, polypropylene, and polystyrene were prepared. MP particles with diameters < 2 mm and < 125 µm were obtained from industrial polymer pellets after cryomilling. Spectra of these mixtures were collected by (i) a process FT-NIR spectrometer equipped with a reflection probe, (ii) by a cw-process Raman spectrometer and (iii) by a time-gated Raman spectrometer using fiber optic probes. For the calibration of chemometric models (partial least squares regression, PLSR) 5 – 10 spectra of defined MP/soil mixtures (consisting of 1 – 4 g material each) were collected. The obtained PLSR models served for the prediction of both, polymer type and content based on the spectra of “unknown” test samples.
Whereas MP could be detected by Raman spectroscopy in coastal sand at 0.5 mass%, in standard soils detection of MP was limited to 10 – 5 mass%. The sensitivity of Raman spectroscopy could be improved by mild treatment with hydrogen peroxide. FT-NIR was suitable for the investigation of MP in standard soils in the range of 5 – 1 mass%, however, here a non-linear effect was observed at higher polymer concentrations. When mixtures of several polymers at low concentration levels were milled together, FT-NIR spectroscopy yielded false positive polymers together with unprecise quantitative information. Recently, the investigation of “real-world” samples shall be tested and compared to the results obtained by micro-FTIR and micro-Raman.
Multivariate data analysis is a universal tool for the evaluation of process spectroscopic data. In process analytics, huge amounts of information are produced i) by the many variables contained in one spectrum often exceeding 1000 (wavenumbers, wavelength, shifts…), and ii) the high number of spectra that is generated within the measurement period. Multivariate data analysis, often also called Chemometrics, help to extract the relevant information which is needed to examine and even control processes. Therefore, calibration models must be precise and robust, and moreover, must cover a wide range of variation of factors posing an influence on the process.
In this pre-conference course an introduction to both, explorative data analysis by PCA (Principal Component Analysis), and regression analysis by the most frequently used method, i.e. PLSR (Partial Least Squares Regression) is given. In principal, all optical spectroscopic methods are suited for multivariate evaluation. It will be demonstrated that under certain preconditions, even process NMR spectra can be predicted by PLSR models.
At first, basic principles of multivariate data analysis will be provided. This includes a short introduction into the concept of model building and interpretation of results. Detailed aspects of data pretreatment and calibration & validation strategies for chemometric models will be provided with own data from NIR, Raman and NMR spectroscopy.
In a first example, the development of an online compatible method for the quantification of methanol in biodiesel by PLSR is presented. This also includes the classification of biodiesel feedstocks by PCA and statistical tools which allow for the estimation of a full uncertainty budget.
The Raman spectroscopic prediction of Hydroformylation reaction in a miniplant is used to discuss shortcomings and pitfalls which may occur with the transfer of off-line models to the real processes. Design of experiment and strategies for suitable lab-scale experiments are presented as a possible way to overcome problems.
In a third application the prediction of the reactants of an esterification reaction based on process NMR data is demonstrated.
Der Beitrag beinhaltet die Vorstellung eines gemeinsamen Projekts zwischen der BAM und der TU Berlin (Process Dynamics and Operations Group). Mittels Raman Spektroskopie werden Hydroformylierungsreaktionen in einer Miniplant der TU prozessbegleitend untersucht. Kalibrationsstrategien, Laboruntersuchungen sowie Vorhersagen von Prozessmessungen basierend auf chemometrischen Modellen werden vorgestellt. Zum gegenwärtigen Zeitpunkt wird die Strategie als erfolgsversprechend bewertet, jedoch sind weitere Untersuchungen insbesondere zum Einfluss der Microemulsion auf die Ramanspektren notwendig.
Abstract. Biodiesel quality control is a relevant issue as biodiesel properties influence diesel engine performance and integrity. Within the European Metrology Research Program (EMRP) ENG09 project “Metrology for Biofuels”, an on-line /at-site suitable near-infrared spectroscopy (NIRS) method has been developed in parallel with an improved EN14110 headspace GC analysis method for methanol in biodiesel. Both methods have been optimized for a methanol content of 0.2 mass% as this represents the maximum limit of methanol content in FAME according to EN 14214:2009. The NIRS method is based on a mobile NIR spectrometer equipped with a fiber-optic coupled probe. Due to the high volatility of methanol, a tailored air-tight adaptor was constructed to prevent methanol evaporation during measurement. The methanol content of biodiesel was determined from evaluation of NIRS spectra by Partial Least Squares Regression (PLS). Both GC analysis and NIRS exhibited a significant dependence on biodiesel feedstock. The NIRS method is applicable to a content range of 0.1 % (m/m) to 0.4 % (m/m) of methanol with uncertainties at around 6% relative for the different feedstocks. A direct comparison of headspace GC and NIRS for samples of FAMEs yielded that the results of both methods are fully compatible within their stated uncertainties.
Der Einsatz moderner Prozessanalysentechnik führt zu einer Datenflut, deren Verarbeitung und Nutzung häufig nicht ohne den Einsatz von Chemometrie zu realisieren ist. Dafür stehen zahlreiche multivariate Verfahren, wie z.B. Hauptkomponentenanalyse (PCA) und Regressionsanalyse (PLSR) für die Klassifizierung und für die quantitative Analyse zur Verfügung. Darüber hinaus können in Spezialfällen, insbesondere in der Spektroskopie, Methoden des „Indirect Hard Modeling“ eingesetzt werden. Anhand einer Industrie-relevanten Reaktion, der Hydroformylierungsreaktion zur Bildung langkettiger Aldehyde aus der Reaktion von Alkenen und Synthesegas, die sowohl im Labormaßstab als auch in einer Miniplant in Kooperation mit der TU Berlin untersucht wurde, werden Kalibrationsstrategien (Design of Experiment, DoE) und Auswertungsansätze mittels PLSR prozessbegleitender Raman Spektren dargelegt. Auf Grundlage des chemometrischen Ansatzes ist eine on-line Überwachung der Reaktion hinsichtlich der Hauptreaktanden, 1-Dodecene und 1-Tridecanal, möglich. Weiterhin konnte mittels Raman Spektroskopie die Bildung eines unerwünschten Nebenprodukts nachgewiesen werden.
Supernatants from a fermentation process of Pichia pastoris were investigated by Raman spectroscopy. Using partial least squares regression, the principal substrates glycerol and methanol could be predicted, however not the expressed protein. To gain further insight, a priori prepared calibration samples were studied by vibrational-, UV/Vis-, and fluorescence spectroscopy. For the quantification of glycerol and methanol, Raman spectroscopy was identified as the most sensitive technique, and superior to near-infrared spectroscopy, but not for protein contents below 1 g L–1. Both UV/Vis absorption and fluorescence spectroscopy are well suited for the quantification of protein, however, best results were obtained with UV/Vis absorption.
Homogen katalysierte Reaktionen sind wichtige Werkzeuge der chemischen Industrie. Milde Reaktionsbedingungen und hohe Selektivitäten führen zu einer energie- und ressourcenschonenden Produktion. Ein bedeutender Prozessschritt ist die Hydroformylierung. Hier kommen Kobalt- und Rhodiumkomplexe mit mehrzähnigen Liganden zum Einsatz, die zumeist in wässriger Lösung vorliegen. Die Anwendbarkeit beschränkt sich demnach auf kurzkettige Edukte mit hinreichender Wasserlöslichkeit.
Ugly Spectra and Lousy interfaces – Challenges for Compact NMR Spectroscopy in Process Control
(2016)
With the introduction of advanced process analytical technology, the closeness of key process variables to their limits can be directly controlled and the product can be classified or even released in real time.
Compact NMR instruments can make NMR spectroscopy accessible in industrial and harsh environments for process control.
Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the Fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522 - 593 nm. Two original technical solutions are used as compared to previous SHS instruments: the use of a high frequency diode pumped solid state (DPSS) laser for excitation of Raman spectra and a microscope-based collection system. Raman spectra are excited at 532 nm at the repetition rate 80 kHz. Raman shifts between 330 cm-1 and 1600 cm-1 are measured. A new application of SHS is demonstrated: for the first time it is used for quantitative Raman analysis to determine concentrations of cyclohexane in isopropanol and glycerol in water. Two calibration strategies are employed: univariate based on the construction of a calibration plot and multivariate based on partial least square regression (PLSR). The detection limits for both cyclohexane in isopropanol and glycerol in water are at a 0.5 mass% level. In addition to the Raman-SHS chemical analysis, classification of industrial oils (biodiesel, poly(1-decene), gasoline, heavy oil IFO380, polybutenes, and lubricant) is performed using their Raman-fluorescence spectra and principal component analysis (PCA). The oils are easily discriminated as they show distinct non-overlapping patterns in the space of principal components.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
The increasing enrichment of water bodies and soils with plastic waste leads to the accumulation of microscopic plastic particles, so-called microplastics (MP). There is an urgent need for analytical methods that help to identify and quantify MP. At present, mainly thermo-analytical and microscopic methods such as micro-infrared spectroscopy or micro-Raman are used for this purpose. The latter are usually tied to time-consuming sample enrichment and preparation, only small sample quantities (micrograms) can be examined and the evaluation of the obtained spectra can be demanding. In the context of this presentation, two approaches are presented which, via the multi-variate analysis of spectroscopic data, allow i) a new methodological approach to screening MP in contaminated soils and ii) an alternative evaluation of large (micro)-spectroscopic data sets.
First, a NIR spectroscopic method is presented which allows MP consisting of polyethylene, polyethylene terephthalate, polypropylene and polystyrene to be detected in the range of up to 0.5 mass percent. Due to short measurement times and robust technology, this approach has the potential, in contrast to thermo-analytical and micro-spectroscopic methods, to examine larger sample quantities with minimal pre-treatment.
The second approach deals with the evaluation of large data sets, as typically obtained as a result of micro-FTIR using modern FPA detectors. The micro-FTIR technique is based on the spectral recording, imaging and subsequent identification of vibration bands typical of synthetic polymers. The image data sets are large and contain spectra of numerous particles of natural and synthetic origin. Exploratory multivariate data analysis has been tested to complement existing approaches based on e.g. spectrum library searches. The core concept used was dimensionality reduction. The results not only represent an orthogonal method for checking the results obtained by an automated library search, but also revealed a group of spectra that were not recorded in the existing spectrum libraries.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environ¬ments for advanced process monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
The catalytic behavior of iron phthalocyanine (FePc)-sensitized magnetic nanocatalysts was evaluated for their application in the oxidative treatment of Bisphenol A (BPA) under mild environmental conditions. Two types of FePc (Fe(II)Pc and Fe(III)Pc), which are highly photosensitive compounds, were immobilized on the surface of functionalized magnetite. The nanomaterials were characterized by high resolution transmission electron microscopy (HR-TEM), X-ray difraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analyses (TGA). The generation of singlet Oxygen by nanomaterials was also investigated. In the presence of UVA light exposure (365nm) and 15mM H2O2, the M@Fe(III)Pc photocatalyst gave the best results; for a catalyst concentration of 2.0gL −1, around 60% BPA was removed after 120min of reaction. These experimental conditions were further tested under natural solar light exposure, for which also M@Fe(III)Pc exhibited enhanced oxidative catalytic activity, being able to remove 83% of BPA in solution. The water samples were less cytotoxic after treatment, this being confrmed by the MCF-7 cell viability assay.
Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples
(2020)
Hyperspectral imaging of environmental samples with infrared microscopes is one of the preferred methods to find and characterize microplastics. Particles can be quantified in terms of number, size and size distribution. Their shape can be studied and the substances can be identified. Interpretation of the collected spectra is a typical problem encountered during the analysis. The image datasets are large and contain spectra of countless particles of natural and synthetic origin. To supplement existing Analysis pipelines, exploratory multivariate data analysis was tested on two independent datasets. Dimensionality reduction with principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) was used as a core concept. It allowed for improved visual accessibility of the data and created a chemical two-dimensional image of the sample. Spectra belonging to particles could be separated from blank spectra, reducing the amount of data significantly. Selected spectra were further studied, also applying PCA and UMAP. Groups of similar spectra were identified by cluster analysis using k-means, density based, and interactive manual clustering. Most clusters could be assigned to chemical species based on reference spectra. While the results support findings obtained with a ‘targeted analysis’ based on automated library search, exploratory analysis points the attention towards the group of unidientified spectra that remained and are otherwise easily overlooked.
Künstliche Mineralfasern (KMF) werden weltweit in vielfältiger Weise als Dämmstoffe verwendet. Jährlich fallen in Deutschland ungefähr 20 Millionen Tonnen KMF an, die nach dem Abriss auf Deponien entsorgt werden müssen. Grundsätzlich besteht jedoch die Möglichkeit der Rückführung dieser Materialien in den Produktionsprozess. Dafür ist es notwendig, anfallende KMF sowohl nach Material (Glaswolle, Steinwolle) als auch nach Alter zu sortieren.
Auf der Suche nach einer Methode, die es grundsätzlich gestattet eine schnelle Untersuchung vor Ort auf einer Baustelle durchzuführen, wurde die Nah-Infrarot (NIR) Spektroskopie unter Verwendung eines Prozessspektrometers mit Reflexionssonde getestet. Anhand von Laboruntersuchungen an 70 KMF Proben, die entweder betriebsneu waren oder bundesweit auf Baustellen gesammelt wurden, konnte eine NIR-basierte Methode zur Unterscheidung in Glas- und Steinwolle entwickelt werden. Mittels Partial Least Square Diskriminanzanalyse (PLS-DA) der NIR Spektren wurde basierend auf 40 zufällig ausgewählten Proben ein chemometrisches Modell kalibriert, das erfolgreich zur Klassifizierung der verbleibenden 30 Proben eingesetzt werden konnte. Unabhängig von Farbe, Dichte oder Alter der untersuchten KMF Proben war es möglich, eine 100% korrekte Zuordnung des Materials zu erzielen. Die nötigen Referenzwerte wurden unter Nutzung eines Röntgenfluoreszenzspektroskopischen Verfahrens über den Elementgehalt nach VDI 3492 ermittelt.
Insgesamt ist der experimentelle Aufwand für die Durchführung und Auswertung der NIR Messungen gering und manifestiert sich vor allem in der Kalibrierphase, die in diesem Fall die Untersuchung von 8-fach aliquotierten Probestücken der 70 KMF umfasste. Nur bei deutlich feuchtem Material muss ein zusätzlicher Trocknungs-schritt berücksichtigt werden. Das vorgestellte Verfahren wurde nachfolgend zu einem Patent angemeldet. Untersuchungen zu einer weiteren Unterscheidung der KMF nach Alter sind geplant.
Für die Rückführung von aus Rückbauprojekten anfallender künstlicher Mineralwolle in ihren Herstellungsprozess ist es notwendig, Stein- und Glaswolle zu unterscheiden und voneinander getrennt zu halten. Zu diesem Zweck wurde das Potential von NIR-Spektroskopie (NIRS) für einen werks- oder baustellenseitigen Einsatz getestet. NIRS wird aufgrund kurzer Messzeiten, minimaler Probenvorbereitung und hoher Robustheit der Spektrometer häufig in der Prozessanalytik eingesetzt. Untersucht wurden 70 verschiedene Mineralwollen, wobei sich der Probenumfang sowohl aus werksneuen Proben mit Herstellerangaben als auch unbekannten Proben ohne Spezifikation zusammensetzen, die auf verschiedenen Baustellen in den Jahren 2016–2017 entnommen oder von Deponien zur Verfügung gestellt worden waren. Als Referenzmethode wurde die Röntgenfluoreszenzanalyse verwendet, um den Mineralwolle-Typ über den Elementgehalt nach VDI 3492 zu identifizieren. Mit Hilfe eines multivariaten Datenanalyseverfahrens konnte schließlich eine Methode etabliert werden, die eine zuverlässige Identifikation von unbekannten Mineralwollen anhand deren NIR-Spektren als Stein- bzw. Glaswolle ermöglicht.
Raman spectroscopy for online monitoring of a homogeneous hydroformylation process in microemulsion
(2021)
An important industrial reaction is hydro¬formylation for the production of aldehydes from alkenes and syngas on the basis of homogeneous catalysis. The main cost factors of the processes currently used are product selectivity and the loss of the catalysts used. Therefore, various concepts for the hydroformylation of long-chain olefins have been developed, including hydroformylation in microemulsions, which is being investigated on a mini-plant scale at the Technical University of Berlin [1]. In this study, online Raman spectroscopy of the reaction of 1-dodecene to 1-tri¬decanal in a microemulsion was performed [2]. First, an experimental design was used to obtain a good representation of the operating range in the mini plant with respect to the concentrations of five reactants in a laboratory setup [3]. Based on the Raman spectra, Partial Least Squares (PLS) models for the prediction of 1-dodecene and 1-tride-decanal were calibrated and with these the reactions were predicted on a laboratory scale. In the next step, the PLS models were applied to online spectra from a mini-plant. This resulted in promising estimates of 1-tridecanal and acceptable predictions of 1-dodecene mass fractions. The predictive power of PLS models in this particular case was limited by unexpected by-product formation which, however, can easily be compensated by an extended calibration. Hence, Raman spectroscopy is a promising technique for process analysis in microemulsions.
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.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and highthroughput mass quantification of microplastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermoanalytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg.
The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most
compact NIR spectrometers available.
In recent years, thermoextraction/desorption-gas chromatography/mass spectrometry (TED-GC/MS) has been developed as a rapid detection method for the determination of microplastics (MP) mass contents in numerous environmentally relevant matrices and, in particular, for the measurement of polymers in water samples without time-consuming sample preparation. The TED-GC/MS method was applied to investigate a typical European municipal wastewater system for possible MP masses. Such investigations are important in view of the recent revision of the Urban Wastewater Treatment Directive. Four different representative sampling sites were selected: greywater (domestic wastewater without toilet), combined sewer, and influent and effluent of a wastewater treatment plant (WWTP). All samples were collected by fractional filtration. Filtration was carried out over mesh sizes of 500, 100, 50, and in some cases, 5 µm. Polyethylene (PE), polypropylene (PP), and polystyrene (PS) were detected in all samples, with the PE fraction dominating in all cases. Styrene-butadiene rubber which serves as an indication of tire abrasion, was only found in the influent of the WWTP. The highest MP mass contents were found in the combined sewer, so MP can become a source of pollution during heavy rain events when the capacity limits of the effluent are reached, and the polluted effluent is released uncontrolled into the environment. Based on the studies, MP retention from the WWTP could be estimated to be approximately 96%. Few trends in polymer type or mass contents were detected within the different fractions of the samples or when comparing samples to each other.
In Deutschland werden jährlich rund 20 Millionen Tonnen künstliche Mineralwolle für die Dämmung von Dächern und Fassaden sowie für Brandschutzprodukte verwendet. Um künstliche Mineralwolle aus Rückbaumaßnahmen in den Produktionsprozess zurückführen zu können, ist eine Trennung in Glas- und Steinwolle erforderlich, um eine Vermischung der beiden Produkte, die unterschiedliche Schmelztemperaturen aufweisen, zu vermeiden. Anstatt die Mineralwolle in spezielle Labors zu schicken, was zeitaufwendig und teuer ist, wurde das Potenzial eines tragbaren Nahinfrarot-Spektrometers (NIR) für den Einsatz vor Ort getestet. 140 verschiedene Mineralwolleproben wurden untersucht, um eine NIR-basierte Methode für die Vor-Ort-Unterscheidung zwischen Glas- und Steinwolle zu etablieren. Die Validierung der Methode mit Testproben und die Anwendung auf Materialien, die aus BigBags für Deponien entnommen wurden, haben gezeigt, dass die Methode präzise und robust ist. Als Referenzmethode wurde die Röntgenfluoreszenzspektroskopie (RFA) mit einer Kalibrierung auf der Grundlage der VDI 3492 verwendet.
The interaction of microcroplastics (MP) with dissolved organic matter, especially humic substances, is of great importance in understanding the behavior of microplastics in aquatic ecosystems. Surface modification by humic substances plays an essential role in transport and interaction of MP with abiotic and biotic components. Previous studies on the interaction between MP and humic substances were largely based on a model compound, humic acid (Sigma-Aldrich). In our work, we therefore investigated the interaction of natural organic matter (NOM) sampled from a German surface water with low-density polyethylene particles (LDPE). Highpressure size exclusion chromatography (HPSEC) and UV/vis absorption and fluorescence spectroscopy were used to characterize the incubation solutions after modifications due to the presence of LDPE, and Raman spectroscopy was used to characterize the incubated microplastics. While the studies of the solutions generally showed only very small effects, Raman spectroscopic studies allowed clear evidence of the binding of humic fractions to MP. The comparison of the incubation of NOM and a lignite fulvic acid which also was tested further showed that specific signatures of the humic substances used could be detected by Raman spectroscopy. This provides an elegant opportunity to conduct broader studies on this issue in the future.
Fenton-like processes using persulfate for oxidative water treatment and contaminant removal can be enhanced by the addition of redox-active biochar, which accelerates the reduction of Fe(III) to Fe(II) and increases the yield of reactive species that react with organic contaminants. However, available data on the formation of non-radical or radical species in the biochar/Fe(III)/persulfate system are inconsistent, which limits the evaluation of treatment efficiency and applicability in different water matrices. Based on competition kinetics calculations, we employed different scavengers and probe compounds to systematically evaluate the effect of chloride in presence of organic matter on the formation of major reactive species in the biochar/Fe(III)/persulfate system for the transformation of the model compound N,N‑diethyl-m-toluamide (DEET) at pH 2.5. We show that the transformation of methyl phenyl sulfoxide (PMSO) to methyl phenyl sulfone (PMSO2) cannot serve as a reliable indicator for Fe(IV), as previously suggested, because sulfate radicals also induce PMSO2 formation. Although the formation of Fe(IV) cannot be completely excluded, sulfate radicals were identified as the major reactive species in the biochar/Fe(III)/persulfate system in pure water. In the presence of dissolved organic matter, low chloride concentrations (0.1 mM) shifted the major reactive species likely to hydroxyl radicals. Higher chloride concentrations (1 mM), as present in a mining-impacted acidic surface water, resulted in the formation of another reactive species, possibly Cl2•−, and efficient DEET degradation. To tailor the application of this oxidation process, the water matrix must be considered as a decisive factor for reactive species formation and contaminant removal.
An accurate measurement of the amount fraction of hydrogen in gas mixtures is mandatory for practical applications, requiring methods that are fast, continuous, robust, and cost-effective. This study compares the performance of Raman and benchtop NMR process spectroscopy for determining the hydrogen amount fraction in gas mixtures. A setup was designed to integrate both techniques, enabling measurements of the same sample.
Tests were conducted with gravimetrically prepared gas mixtures of reference quality ranging from 1.20 cmol/mol to 85.83 cmol/mol of hydrogen. The results demonstrate that Raman spectroscopy provides superior performance, with a minimal root mean square error (RMSE) of 0.22 cmol/mol and excellent linearity. In contrast, benchtop NMR spectroscopy faced challenges, such as overlapping peaks and longer measurement times, resulting in a higher RMSE of 0.71 cmol/mol. Raman spectroscopy proves to be particularly well-suited for
practical applications due to its high accuracy and linearity. Meanwhile, benchtop NMR spectroscopy holds potential for future enhancements through ongoing technological advances, such as higher magnetic field strengths. In summary, the results from our study indicate that Raman spectroscopy is already a serviceable method for precise hydrogen quantification, whereas benchtop NMR spectroscopy can be attributed potential for future applications.
AbstractA prerequisite for the recycling of mineral wool is information about the type of material, that is, whether it is glass wool or rock wool. As mineral wool produced before the year 2000 can be potentially carcinogenic, it is furthermore important to distinguish between “old” and “new” wool when handling the material. Based on VDI 3492, it is possible to determine the material and, in the case of rock wool, the age by analyzing the mass fractions of eight oxides, which are the main components of mineral wool. This study presents the X‐ray fluorescence (XRF) analyses of 141 man‐made mineral fibers collected in Germany. Only in a few cases it was not possible to clearly assign the material type. In contrast, the identification of “old” and “new” rock wool posed a challenge as there were many borderline samples. Based on the available data, a chemometric model was developed that can classify “old” and “new” RW with a sensitivity of 93% and 89% and with a specificity of 100% in both cases. However, care must be taken when oxide contents are close to the specification limits. The reason for this mainly lies in the overlapping intervals of key oxides as suggested by VDI 3492, and, to a lesser extent, in the uncertainties typically occurring in the XRF‐based analysis of oxides. With this study, a comprehensive collection and evaluation of XRF data on mineral wool is made available, which can serve as a reference database for future users.