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Im Verbundprojekt RUSEKU wurde die Probenahme von Wasserproben entscheidend weiterentwickelt. Wichtig ist hierbei zu gewährleisten, dass genügend Merkmalsträger in jeder Probe untersucht werden. Es muss daher eine für den Probenahmeort repräsentative Beprobung hinsichtlich des Wasservolumens in Abhängigkeit der Partikelanzahl gewährleistet sein.
Das Hauptaugenmerk lag im vorliegenden Projekt auf einer praxisnahen Beprobungsstrategie. Es wurden verschiedene Konzepte ausprobiert.
• Grundsätzlich hat sich gezeigt, dass eine Stichprobe eine Momentaufnahme des MP-Massengehaltes zeigt. Es wird eine hohe Statistik, also eine Vielzahl an Messungen am gleichen Probenahmeort, benötigt, um eine valide Aussage über den MP-Gehalt zu machen.
• Es zeigt sich, dass eine integrale Probenahme über mehrere Wochen mit dem SK routinemäßig möglich ist. Die erfassten MP-Massen sind reproduzierbar und robust.
• Die DFZ ist für Stichproben geeignet. Partikel < 50 µm werden eventuell unterschätzt
• Die fraktionierte Filtration kann für Stich- und Mischproben direkt im Feld genutzt werden. Fraktionen von 10 und 5 µm werden später im Labor Vakuum filtriert. Es erfolgt eine Fraktionierung der Probe mit Siebmaschenweiten von 1000, 500, 100, 50, 10 und 5 µm.
• Die fraktionierte Filtration kann auch anschließend an die Beprobung mit dem SK zur Anwendung kommen. Wird die mit dem SK gewonnene Wasserprobe fraktioniert filtriert, kann neben einem MP-Gesamtgehalt auch eine Einschätzung über die Partikelgrößen gewonnen werden.
• Für Wässer mit geringen Partikelzahlen wurde ein Messfiltertiegel entwickelt. Dieser hat eine Maschenweite von 6 µm. Seine Anwendung kann mögliche Verluste beim Transferieren vom Probenahmetool zum Messgefäß und mögliche Kontaminationen reduzieren.
Die Optimierung der Probenahmestrategie wurde durch Modellversuche und Simulationen unterstützt. Modellversuche zum Sinkverhalten und Simulationen von MP in realen Gewässern verdeutlichten das komplexe Verhalten der Partikel. Es konnte gezeigt werden, dass Partikel ab einer bestimmten Größe (und kleiner) bei genügend starker Turbulenz sich in der Wassersäule unabhängig von ihrer Dichte verhalten und so auch MP mit kleiner Dichte (z.B. PE) in der gesamten Wassersäule zu finden sind. Es konnte mit dem TEM die Existenz von NP gezeigt werden.
Ein wesentlicher Aspekt des RUSEKU Projektes war die Beprobung realer Kompartimente. Beprobt wurde neben Oberflächengewässern, das urbane Abwassersystem der Stadt Kaiserslautern, Waschmaschinenabwasser und Flaschenwasser.
• In Oberflächengewässern wurde hauptsächlich PE gefunden. Je nach Probe und Gewässer konnten auch PP, PS, PET, PA, SBR und Acrylate nachgewiesen werden.
• Im urbanen Abwassersystem der Stadt Kaiserslautern konnte an allen Probenahmestandorten MP nachgewiesen werden. Es wurde hauptsächlich PE, neben geringeren Mengen an PP, PS und SBR gefunden. Nach einem Regenereignis war der SBR Anteil deutlich erhöht.
• Die Beprobung eines realen Wäschepostens, bestehend aus T-Shirts und Hemden mit PA/CO oder PES/CO Mischgewebe, zeigte einen PA- und PES-Austrag im Waschwasser. Der überwiegende Teil der detektierten Fasern ist aber auf Baumwolle zurückzuführen. Reine gravimetrische Messungen zur Detektion von MP führen zu einer starken Überschätzung.
• In Flaschenwasser (PET-Flaschen) konnte MP detektiert werden. PET wurde nur im stillen Mineralwasser, nicht in Mineralwasser mit Kohlensäure gefunden werden. Teilweise wurde auch das MP-Material des Verschlusses im Wasser detektiert.
• Für Luftproben konnte ein Aufbau zur größenselektiven Beprobung getestet werden.
Neben der Probenahme hat das Projekt auch gezeigt, dass die TED-GC/MS geeignet für die MP-Detektion im Routinebetrieb ist. Die TED-GC/MS konnte weiter optimiert werden. Es wurden MP-Massen bestimmt.
Im Projekt wurden erste realitätsnahe Referenzmaterialien für die MP Detektion hergestellt. Die Herstellung von realitätsnahen Polymeren in ausreichender Homogenität und Menge hat sich als große Herausforderung herausgestellt.
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.
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.
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.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
µ-FTIR spectroscopy is a widely used technique in microplastics research. It allows to simultaneously characterize the material of the small particles, fibers or fragments, and to specify their size distribution and shape. Modern detectors offer the possibility to perform two-dimensional imaging of the sample providing detailed information. However, datasets are often too large for manual evaluation calling for automated microplastic identification. Library search based on the comparison with known reference spectra has been proposed to solve this problem.
To supplement this ‘targeted analysis’, an exploratory approach was tested. Principal component analysis (PCA) was used to drastically reduce the size of the data set while maintaining the significant information. Groups of similar spectra in the prepared data set were identified with cluster analysis. Members of different clusters could be assigned to different polymer types whereas the variation observed within a cluster gives a hint on the chemical variability of microplastics of the same type. Spectra labeled according to the respective cluster can be used for supervised learning. The obtained classification was tested on an independent data set and results were compared to the spectral library search approach.
Working towards a comprehensive understanding of introduction pathways, number, and fate of micro¬plastics in the environment, suitable analytical methods are a precondition. Micro-spectroscopic methods are probably the most widely used techniques. Besides their ability to measure single spectra of a particle or fiber, most modern FTIR- and Raman microscopes are also capable of two-dimensional imaging. This is very appealing to microplastics research because it allows to simultaneously characterize the analytes chemically as well as their size (distribution) and shape.
Two-dimensional imaging on extensive sample areas with FTIR-micros¬copes is facilitated by focal plane array (FPA) detectors resulting in large data sets comprised of up to several million spectra. With numbers too large for manual inspection of each individual spectrum, automated data evaluation is inevitable. Identifying different polymers based on the comparison with known reference spectra (library search) has proven to be a suitable approach. For that purpose, FTIR-spectra of common plastics can be collected to create an individual reference library.
To Supplement this ‘targeted analysis’, looking for known substances via library search, an exploratory approach was tested. Principal component analysis (PCA) proved to be a helpful tool to drastically reduce the size of the data set while maintaining the significant information. Subsequently, cluster analysis was used to find groups of similar spectra. Spectra found in different clusters could be assigned to different polymer types. The variation observed within clusters gives a hint on chemical variability of microplastics of the same polymer found in the sample. Spectra labeled according to the respective cluster/polymer type were used to build a classification model which allowed to quickly predict the polymer type based on the FTIR spectrum. Classification was tested on a second, independent data set and results were compared to the spectral library search procedure.
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.
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.