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Volatile organic compounds (VOCs) emitted by furniture and building materials can cause health issues. For an improvement of indoor air quality low emitting materials should be used. Quality assurance and –control (QA/QC) measures require an emission reference material (ERM) with a predictable emission rate of VOCs. The idea is to use porous materials as ERM, which store the VOCs inside their pores and emit them constantly.
Emission testing of volatile organic compounds (VOC) from materials and products is commonly based on emission test chamber measurements. To ensure the comparability of results from different testing laboratories their measurement performance must be verified. For this purpose, Bundesanstalt für Materialforschung und -prüfung (BAM) organizes an international proficiency test every two years using well-characterised test materials (one sealant, one furniture board and four times a lacquer) with defined VOC emissions. The materials fulfilled the requirements of homogeneity, reproducibility, and stability. Altogether, 41 VOCs were included of which 37 gave test chamber air concentrations between 10 and 98 µg/m³. This is the typical concentration range to be expected and to be quantified when performing chamber tests. Four compounds had higher concentrations between 250 and 1105 µg/m³. The relative standard deviations (RSD) of BAM proficiency tests since 2008 are compared and the improvement of the comparability of the emission chamber testing is shown by the decrease of the mean RSD down to 23% in 2021. In contrast, the first large European interlaboratory comparison in 1999 showed a mean RSD of 51%.
The emission of ultrafine particles from small desktop Fused Filament Fabrication (FFF) 3D printers has been frequently investigated in the past years. However, the vast majority of FFF emission and exposure studies have not considered the possible occurrence of particles below the typical detection limit of Condensation Particle Counters and could have systematically underestimated the total particle emission as well as the related exposure risks. Therefore, we comparatively measured particle number concentrations and size distributions of sub-4 nm particles with two commercially available diethylene glycol-based instruments – the TSI 3757 Nano Enhancer and the Airmodus A10 Particle Size Magnifier. Both instruments were evaluated for their suitability of measuring FFF-3D printing emissions in the sub-4 nm size range while operated as a particle counter or as a particle size spectrometer. For particle counting, both instruments match best when the Airmodus system was adjusted to a cut-off of 1.5 nm. For size spectroscopy, both instruments show limitations due to either the fast dynamics or rather low levels of particle emissions from FFF-3D printing in this range. The effects are discussed in detail in this article. The findings could be used to implement sub-4 nm particle measurement in future emission or exposure studies, but also for the development of standard test protocols for FFF-3D printing emissions.
Mobile proximal gamma-ray spectrometry (GS) is an emerging approach to estimate soil texture directly in the field. It relies on gamma radiation emitted during the natural decay of potassium-40 (K-40) and other isotopes. The K-40 counts are correlated with total K content, mineralogical soil composition and therefore with soil texture. Yet, it is not clear if K fertilisation impacts the ratio of K-40 to total K which would necessitate to take the fertilisation history into account for soil sensing applications. To elucidate this question, a well-documented long-term experiment was selected. The soils of the Rengen grassland experiment (55 plots) were investigated for total K (XRF-K) and for K-40 via mobile proximal GS. No significant differences in XRF-K and K-40 were found between the treatments with and without K fertilisation, although formely published results point to negative and positive K balances, respectively. Fertiliser analysis revealed a ratio of K-40 to total K that was almost identical to the natural abundance of 0.0117%. It was concluded that it is not necessary to take K fertilisation history into account when predicting soil texture from mobile proximal GS on agricultural land for, e.g., precision farming purposes.
Fused filament fabrication (FFF) on desktop 3D printers is a material extrusion-based technique often used by educational institutions, small enterprises and private households. Polymeric filaments are melted and extruded through a heated nozzle to form a 3D object in layers. The extrusion temperature is therefore a key parameter for a successful print job, but also one of the main driving factors for the emission of harmful air pollutants, namely ultrafine particles and volatile organic gases, which are formed by thermal stress on the polymeric feedstock. The awareness of potential health risks has increased the number of emission studies in the past years. However, the multiplicity of study designs makes an objective comparison of emission data challenging because printer hardware factors such as the actual extruder temperature (TE) and also feedstockspecific emissions are not considered. We assume that across the market of commercial low- and mid-price FFF printers substantial deviations between actual and set extruder temperatures exist, which have a strong effect on the emissions and hence may bias the findings of exposure studies. In our last publication, we presented a standardized feedstock-specific emission test method and showed that for each investigated feedstock an increase in actual extruder temperature was accompanied by an increase in particle emissions (Tang and Seeger, 2022). Therefore, any systematic discrepancy between set and actual extruder temperature matters. In this study, we used a thermocouple and an infrared camera to measure the actual extruder temperatures at different heights. We found significant under- and overestimation of the actual extruder temperatures by the respective set temperatures in three commercial printers. This caused a broad variation of the measured total numbers of emitted particles (TP), even when the same feedstock was operated. For the determination of TP, we followed the DE-UZ 219 test guideline. In a second round we repeated the tests with all printers adjusted to exactly the same extruder temperatures, i.e., to TE=230°C for ABS and TE=210°C for PLA. All measurements were conducted in a 1 m³ emission test chamber. Particle emissions in the size range between 4 nm and 20 μm were detected. Printing on three different printer models without temperature adjustment resulted for each of the investigated feedstocks in a variation in TP of around two orders of magnitude. After temperature adjustment, this was substantially reduced to approx. one order of magnitude and hence minimizes the bias of printer hardware on the emissions. Our findings suggest that adjustment of the extruder temperature should be mandatory in emission testing standards. It also poses a more accurate benchmark and provides more reliable emission data for evaluation of indoor air quality or for health risk assessments. In addition, a proper temperature setting is in the interest of the user. Some commercial FFF printers may have a higher actual extruder temperature than displayed and unintended overheating may not only impair the print quality but may cause unnecessarily increased exposure to particle emissions.
On the use of Carbograph 5TD as an adsorbent for sampling VVOCs: validation of an analytical method
(2023)
A standardised method for the analysis of very volatile organic compounds (VVOCs) in indoor air is still missing. This study evaluates the use of Carbograph 5TD as an adsorbent for 60 compounds (47 VVOCs + 13 VOCs) by comparing their recoveries with different spiking modes. The influence of the spiking of the tubes in dry nitrogen, humidified air or along the whole flushing duration mimicking real sampling was investigated. 49 substances (36 VVOCs from C1 to C6) had recoveries over 70% on the adsorbent in humidified air and were validated. The linearity of the calibration curves was verified for every spiking mode and the limits of detection (LOD) and quantification (LOQ) were determined. The LOQs were lower than the existing indoor air guideline values. The robustness of the method was considered by studying the influence of the sampling volume, the sampling flow rate, the humidity level and the storage of the tubes. In general, the most volatile or polar substances were the less robust ones. The combined measurement uncertainty was calculated and lies below 35% for a vast majority of the substances. An example of an emission chamber test using polyurethane foam is shown: Carbograph 5TD performs much better than Tenax® TA for VVOCs and emissions from n-butane were quantified with combined measurement uncertainty.
Volatile organic compound (VOC) emissions from consumer products contribute to human inhalation exposure and may cause adverse health effects. Existing methods to determine long-term VOC emissions from e.g. building products need to be verified for their suitability to reliably detect initial VOC emissions from consumer products within the first hours and days of use, which would facilitate realistic inhalation exposure assessments. To investigate this issue, VOCs emitted from a test sample were determined in a large-scale emission test chamber and in two micro-chambers of different volumes, and the results were compared.
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.
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.
Fused filament fabrication (FFF) is a material extrusion-based technique often used in desktop 3D printers. Polymeric filaments are melted and are extruded through a heated nozzle to form a 3D object in layers. The extruder temperature is therefore a key parameter for a successful print job but also one of the main emission driving factors as harmful pollutants (e.g., ultrafine particles) are formed by thermal polymer degradation. The awareness of potential health risks has increased the number of emission studies in the past years. However, studies usually refer their calculated emission data to the printer set extruder temperature for comparison purposes. In this study, we used a thermocouple and an infrared camera to measure the actual extruder temperature and found significant temperature deviations to the displayed set temperature among printer models. Our result shows that printing the same filament feedstocks with three different printer models and with identical printer set temperature resulted in a variation in particle emission of around two orders of magnitude. A temperature adjustment has reduced the variation to approx. one order of magnitude. Thus, it is necessary to refer the measured emission data to the actual extruder temperature as it poses a more accurate comparison parameter for evaluation of the indoor air quality in user scenarios or for health risk assessments.
Ratiometric green–red fluorescent nanosensors for fluorometrically monitoring pH in the acidic range were designed from 80 nm-sized polystyrene (PS) and silica (SiO2) nanoparticles (NPs), red emissive reference dyes, and a green emissive naphthalimide pH probe, analytically and spectroscopically characterized, and compared regarding their sensing performance in aqueous dispersion and in cellular uptake studies. Preparation of these optical probes, which are excitable by 405 nm laser or LED light sources, involved the encapsulation of the pH-inert red-fuorescent dye Nile Red (NR) in the core of self-made carboxylated PSNPs by a simple swelling procedure and the fabrication of rhodamine B (RhB)-stained SiO2-NPs from a silane derivative of pH-insensitive RhB. Subsequently, the custom-made naphthalimide pH probe, that utilizes a protonation-controlled photoinduced electron transfer process, was covalently attached to the carboxylic acid groups at the surface of both types of NPs. Fluorescence microscopy studies with the molecular and nanoscale optical probes and A549 lung cancer cells confirmed the cellular uptake of all probes and their penetration into acidic cell compartments, i.e., the lysosomes, indicated by the switching ON of the green naphthalimide fluorescence. This underlines their suitability for intracellular pH sensing, with the SiO2-based nanosensor revealing the best performance regarding uptake speed and stability.
Detailed knowledge about soil composition is an important prerequisite for many applications, for example precision agriculture. Current standard laboratory methods are complex and time-consuming but could be complemented by non-invasive optical techniques. Its capability to provide a molecular fingerprint of individual soil components makes Raman spectroscopy a very promising candidate. A major challenge is strong fluorescence interference inherent to soil, but this issue can be overcome effectively using shifted excitation Raman difference spectroscopy (SERDS). A customized dual-wavelength diode laser emitting at 785.2 and 784.6 nm was used to investigate 117 soil samples collected from an agricultural field along a distance of 624 m and down to depths of 1 m. To address soil spatial heterogeneity, a raster scan approach comprising 100 measurement spots per sample was applied. Based on the Raman spectroscopic fingerprint extracted from intense fluorescence interference by SERDS, 13 mineral soil constituents were identified, and even closely related molecular species could be discriminated, for example polymorphs of titanium dioxide and calcium carbonate. For the first time, the capability of SERDS is demonstrated to predict the calcium carbonate content as an important soil parameter using partial least squares regression (R2 = 0.94, root mean square error of cross-validation RMSECV = 2.1%). Our findings demonstrate that SERDS can extract a wealth of spectroscopic information from disturbing backgrounds enabling qualitative and quantitative soil analysis. This highlights the large potential of SERDS for precision agriculture but also in further application areas, for example geology, cultural heritage and planetary exploration.
Around 2.5 million tons of municipal sewage sludge (dry matter) are produced in Ger-many every year. The disposal or recycling of this mass often confronts plant operators with considerable problems. In addition to organic substances, sewage sludges also contain important inorganic nutrients that can be recycled. Due to the finite nature of phosphorus as a resource and in favor of natural material cycles, it makes sense to utilize the phosphorus bound in sewage sludge. Therefore, Phosphorus recycling from sewage sludge will be obligatory in Germany from 2029. However, the pollutants pre-sent in sewage sludge, such as heavy metals and organic trace pollutants, are prob-lematic. In many cases, the sewage sludge is incinerated, removing organic compo-nents, and leaving a mineral residue. Various processes exist for the reprocessing of sewage sludge ashes. Reliable analytics and process monitoring are required for all of them.
In this context, the first and most important task of BAM regarding new materials or processes is safety in technology and chemistry through validated and correct analyt-ics. Since there are no reference materials for sewage sludge ashes so far, it is not possible to make any statements about the extent to which the measured values of the comparative measuring methods are correct at all. Therefore, suitable, and representa-tive reference materials are indispensable for the validation of the measurement results and the quality assurance during the entire recycling process. Great care is required to select and prepare these reference materials. The reference materials must corre-spond in matrix to the samples that are to be investigated in the industrial process. An iron-rich and an aluminum-rich sewage sludge ash from mono-incineration plants were selected for this purpose. 10 kg of each sewage sludge ash were homogenized, char-acterized, and prepared. The main, minor and trace element compositions of the sew-age sludge ashes are determined by different measuring methods. The candidate ref-erence materials will be available as BAM-U200 und BAM-U201.
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.
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.
Rückgebaute Mineralwolledämmstoffe und Baustellenverschnitte werden in der Regel deponiert und damit als Rohstoffe dem Markt entzogen. Ziel dieses Projektes ist es darzulegen, dass das Recycling von Glas- und Steinwolle im großmaßstäblich volumenrelevanten Umfang für das Schmelzwannenverfahren technisch umsetzbar und ökonomisch und ökologisch vorteilhaft ist. Neben verfahrenstechnischen Herausforderungen, gilt es die wirtschaftliche in-situ-Identifikation unbekannter Mineralwolle zu ermöglichen. Dazu wird auf erste erfolgversprechende Tastversuche mit spektroskopischen Methoden weiter aufgebaut.
Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization.
Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements.
A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2 s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2 s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation Analysis for liquid samples (original and filtered) resulted in lower R2 s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
The Intelligence for Soil (I4S) project deals with the development of an integrated system for the site-specific soil fertility management. One goal of the project is to establish a mobile sensor platform for soil mapping. The task here is the installation and optimization of a XRF sensor for online measurement directly on the field. The fast soil mapping allows later on a site-specific fertilisation of essential plant nutrients like K, Ca and P. In order to optimize the application of the fertilizers it is necessary to relate the total contents analysed with XRF to the soil nutrients which are available to the plants. For this purpose the plant available phosphate supply was analysed with the Calcium-Acetate-Lactate extraction method (CAL) according to VDLUFA and compared with the total contents.
The project “Intelligence for Soil” (I4S) aims at the design of an integrated system for improvement of soil functions and fertilizer recommendations. This system is composed by different sensors that will provide a detailed assessment of soil properties and processes, which are prerequisites for a site-specific, resource-saving and ecofriendly soil management, considering the soil as a sustainable resource for the bioeconomy. One of these sensors will be an energy-dispersive X-Ray Fluorescence spectrometer. It is a non-destructive technique suitable for in-situ measurements due to a minimum sample preparation and it allows fast multielement analyses. In this work, an automatic system has been developed using a handheld equipment from Olympus (Vanta C series). A polypropylene (PP) film was used to protect the measuring window of the device from dust and possible cross-contamination. To control the stepper motor that unrolls the PP film, a microcontroller was used to ensure that a piece of clean PP is in front of the measuring window for each new analysis.
Preliminary calibration studies using pre-defined methods, Geochem and Soil Methods, were performed with the following Certified Reference Materials (CRMs): NRC Till 1-3, NIST 2710, BAM U110, ERM CC141, BCR 142R, IAEA Soil 7. The CRMs were measured 10 times at different spots. The spot size was 10 mm in diameter, irradiation time was 60 ? s. The averaged data from X ray characteristic emission line intensities for Al, P, Si, Ca, Fe, Mn, Zn, Cu and Ni Kα were in close agreement with certified mass fraction data. The linear correlation coefficients (r) ranged from 0.852 for P to 0.999 for Mn. A second round of calibration studies were performed with the following CRMs: GBW07402, GBW 07405, NCS DC 73023, NCS DC 73030, NCS DC 85109, NCS DC 87104, NIST 1646a, NIST 2704, NIST 2710, NRC Till 1-3, VS 2498-83, and the same pre-defined methods and experimental parameters were employed. The results of the 13 CRMs were now compared with the values obtained by a validated WDXRF method. The linear correlation coefficients (r) ranged from 0.998 for Ca to 0.999 for Zn. Other elements such as S, K and Ti can also be properly determined, but validation still requires more robust calibration models.
Further calibration studies will be performed in order to circumvent matrix effects and to guarantee reliable results. Besides that, the automatic system will be placed on a mobile sensor platform and the system will be tested in the field. A guidance for on site-specific fertilization integrating the results obtained from different sensors placed on the platform is expected.
Gegenstand dieser Arbeit ist die Untersuchung eines bestehenden pyrometallurgischen Prozesses zur Rückgewinnung von Tantal und Niob aus metallurgischen Reststoffen mit vorwiegend niedriger Wertstoffkonzentration.
Zur näheren Erforschung der im vorliegenden Stoffsystem ablaufenden Reduktionsprozesse wurden in einem Elektrolichtbogenofen Schmelzversuche im Pilotmaßstab durchgeführt. Als Reduktionsmittel diente Koks, welcher mithilfe einer Argon-gespülten Eisenlanze in die flüssige mineralische Schmelze eingebracht wurde. Während der Reduktionsbehandlung werden Refraktärmetalle wie Tantal und Niob in ihre Carbide überführt und anschließend in der erschmolzenen eisenbasierten Metallphase am Boden des Reaktors angereichert. Neben Tantal und Niob gelangt auch ein Teil des im Einsatzmaterial enthaltenen Titans als unerwünschtes Begleitelement in die Metallphase.
Sein Großteil verbleibt jedoch als Oxid in der Schlacke und wird dort hauptsächlich im Mineral Perowskit (CaTiO3) gebunden. Die erstarrten Schlackeproben wurden mit verschiedenen Methoden wie Röntgenfluoreszensanalyse, Röntgenbeugung und Rasterelektronenmikroskopie mit gekoppelter energiedispersiver Röntgenanalyse untersucht, um die Bildung tantalhaltiger Mineralphasen zu verschiedenen Stadien des Reduktionsprozesses zu verfolgen.
Die hier gewonnenen Erkenntnisse zeigen, dass weniger die durch das Einblasen von Koks verursachte Reduktionsreaktion, sondern das Absinken der tantalreichen Eisentröpfchen in der flüssigen mineralischen Schmelze, gefolgt von ihrer Anreicherung in die Metallphase, für die Kinetik ausschlaggebend ist.
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.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umwelt-beständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe (polybromierte Flammschutzmittel) in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
Die vom BMBF unterstütze Forschungsinitiative BonaRes entwickelt Strategien um Boden als nachhaltige Ressource für die Bioökonomie zu nutzen. Das Projekt Intelligence for Soil (I4S) ist ein Teil dieser Initiative und beschäftigt sich mit der Entwicklung eines integrierten Systems zur ortsspezifischen Düngung. Ein Ziel dieses Projektes ist die Etablierung einer mobilen Sensorplattform zur Bodenkartierung. Aufgabe der BAM ist dabei das Installieren und die Optimierung eines RFA Sensors für die online Messung direkt auf dem Feld. Die RFA ist eine zerstörungsfreie Technik, die kaum Probenvorbereitung benötigt und einen schnelle Multielement Analyse ermöglicht. Das schnelle Kartieren des Bodens ermöglicht später eine ortsspezifische Düngung mit Nährstoffen wie z.B. K, Ca und P.
Die vom BMBF unterstütze Forschungsinitiative BonaRes entwickelt Strategien um Boden als nachhaltige Ressource für die Bioökonomie zu nutzen. Das Projekt Intelligence for Soil (I4S) ist ein Teil dieser Initiative und beschäftigt sich mit der Entwicklung eines integrierten Systems zur ortsspezifischen Düngung. Ein Ziel dieses Projektes ist die Etablierung einer mobilen Sensorplattform zur Bodenkartierung. Aufgabe der BAM ist dabei das Installieren und die Optimierung eines RFA Sensors für die online Messung direkt auf dem Feld. Die RFA ist eine zerstörungsfreie Technik, die kaum Probenvorbereitung benötigt und einen schnelle Multielement Analyse ermöglicht. Das schnelle Kartieren des Bodens ermöglicht später eine ortsspezifische Düngung mit Nährstoffen wie z.B. K, Ca und P.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set.
Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umwelt-beständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
In addition to previously reported results on the accelerated weathering of polystyrene samples (PS) containing 1 wt.% hexabromocyclododecane (HBCD), we present the first results of our investigations of polypropylene samples (PP) containing 0.1 wt.% BDE-209. All studied polymer samples were exposed to a defined weathering schedule in a climate chamber in accordance to regulation EN ISO 4892-3:2006. For the determination of BDE-209 in the collected raining water the samples were prepared in accordance to a validated protocol. Before the analyses each sample was spiked with isotopically labeled BDE-209. Subsequently the samples were extracted with isooctane. The obtained extracts were concentrated, and the resulting solutions were analyzed by GC/MS. Additionally, the total bromine content was monitored for the weathered and untreated samples using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and X-ray fluorescence analysis (XRF) as a non-destructive and rapid method. In general, the resulting data from the accelerated weathering will be compared to those from the natural weathering experiments. Here, the surfaces of the test pieces were analyzed by LA-ICP-MS and XRF as well. Moreover, soil bed tests were conducted in a well characterized model soil. This soil (boulder-clay, sand with 12 % loam, particle size in total 0.2-4 mm) is filled in a free-draining basin inside of an air-conditioned cellar. In this manner, TOC, water capacity and humidity are recorded parameters. To induce a leaching process from the samples by the raining water, the target water content is calculated to 8%. The actual humidity is measured by a tensiometer, assuring the duration of the raining period. A defined humidity is a fundamental parameter for a biological activity in the soil. The test polymers were placed up to the half in the soil. Microbial activity of the soil is monitored by a reference polymer (polyurethane) and should induce the release of HBCD and BDE-209 out of the test materials. These released analytes will be captured by passive samplers (silicone tubes) placed in a distinct distance to the polymer samples in the soil. The soil bed experiments are complementary to the weathering experiments due to the biological activity in the soil.
In addition to previously reported results on the accelerated weathering of polystyrene samples (PS) containing 1 wt.% hexabromocyclododecane (HBCD), we present the first results of our investigations of polypropylene samples (PP) containing 0.1 wt.% BDE-209. All studied polymer samples were exposed to a defined weathering schedule in a climate chamber in accordance to regulation EN ISO 4892-3:2006. For the determination of BDE-209 in the collected raining water the samples were prepared in accordance to a validated protocol. Before the analyses each sample was spiked with isotopically labeled BDE-209. Subsequently the samples were extracted with isooctane. The obtained extracts were concentrated, and the resulting solutions were analyzed by GC/MS. Additionally, the total bromine content was monitored for the weathered and untreated samples using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and X-ray fluorescence analysis (XRF) as a non-destructive and rapid method. In general, the resulting data from the accelerated weathering will be compared to those from the natural weathering experiments. Here, the surfaces of the test pieces were analyzed by LA-ICP-MS and XRF as well. Moreover, soil bed tests were conducted in a well characterized model soil. This soil (boulder-clay, sand with 12 % loam, particle size in total 0.2-4 mm) is filled in a free-draining basin inside of an air-conditioned cellar. In this manner, TOC, water capacity and humidity are recorded parameters. To induce a leaching process from the samples by the raining water, the target water content is calculated to 8%. The actual humidity is measured by a tensiometer, assuring the duration of the raining period. A defined humidity is a fundamental parameter for a biological activity in the soil. The test polymers were placed up to the half in the soil. Microbial activity of the soil is monitored by a reference polymer (polyurethane) and should induce the release of HBCD and BDE-209 out of the test materials. These released analytes will be captured by passive samplers (silicone tubes) placed in a distinct distance to the polymer samples in the soil. The soil bed experiments are complementary to the weathering experiments due to the biological activity in the soil.
Environmental sustainability and –stability of materials concerning the migration of pollutants
(2019)
In addition to previously reported results on the accelerated weathering of polystyrene samples (PS) containing 1 wt. % hexabromocyclododecane (HBCD), we present the first results of our investigations of polypropylene samples (PP) containing 0.1 wt. % BDE-209. All studied polymer samples were exposed to a defined weathering schedule in a climate chamber in accordance to regulation EN ISO 4892-3:2006. For the determination of BDE-209 in the collected raining water the samples were prepared in accordance to a validated protocol. Before the analyses each sample was spiked with isotopically labeled BDE-209. Subsequently the samples were extracted with isooctane. The obtained extracts were concentrated, and the resulting solutions were analyzed by GC/MS. Additionally, the total bromine content was monitored for the weathered and untreated samples using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and X-ray fluorescence analysis (XRF) as a non-destructive and rapid method. In general, the resulting data from the accelerated weathering will be compared to those from the natural weathering experiments. Here, the surfaces of the test pieces were analyzed by LA-ICP-MS and XRF as well. Moreover, soil bed tests were conducted in a well characterized model soil. This soil (boulder-clay, sand with 12 % loam, particle size in total 0.2-4 mm) is filled in a free-draining basin inside of an air-conditioned cellar. In this manner, TOC, water capacity and humidity are recorded parameters. To induce a leaching process from the samples by the raining water, the target water content is calculated to 8%. The actual humidity is measured by a tensiometer, assuring the duration of the raining period. A defined humidity is a fundamental parameter for a biological activity in the soil. The test polymers were placed up to the half in the soil. Microbial activity of the soil is monitored by a reference polymer (polyurethane) and should induce the release of HBCD and BDE-209 out of the test materials. These released analytes will be captured by passive samplers (silicone tubes) placed in a distinct distance to the polymer samples in the soil. The soil bed experiments are complementary to the weathering experiments due to the biological activity in the soil.
The need for sustainable and effective usage of limited resources like water and soil becomes more and more important due to increasing populations and their need for food. The research initiative BonaRes funded by the Federal Ministry of Education and Research, develops strategies to use soil as a sustainable resource for the bio-economy. One part of this initiative is the Intelligence for soil (I4S) project. It deals with the development of an integrated system for the site-specific soil fertility management. The goal of the project is to establish a mobile sensor platform for soil mapping as well as to apply soil and crop models to deal with the high amount of produced data. These models are going to be used to formulate recommendations on the use of fertilizers and to gain an improvement on the soil fertility. Using these information’s, the environmental pollution with substances like nitrate contained in fertilizers can be decreased. Furthermore, the costs for farmers can be decreased if only the amounts of fertilizer actually needed are applied to the field.
The task for the Federal Institute for Materials Research and Testing (BAM) is the installation and optimization of a handheld XRF sensor for the online measurement directly on the field. It is a non-destructive technique and quite suitable for the online approach due to a low maintenance in the sample preparation and a fast and simultaneous multi element analysis. The elements of interest in this case are light elements like phosphorus, calcium and potassium. One of the major tasks is the calibration of the sensor for different soils, therefore, calibration models are evolved to deal with different types of soil. Due to the multi element analysis, micro and macro nutrients can be determined simultaneously. Those differ a lot in their contents which makes it inevitable to optimize and adapt the measuring procedure. By analyzing soil, its always important to deal with matrix effects, that is why the influence of different disturbances like the moisturize content and the grain size needs to be investigated.
The XRF is only one of many sensors that combined give customized fertilizing recommendations in the end.
The need for sustainable and effective usage of limited resources like water and soil becomes more and more important due to increasing populations and their need for food. The research initiative BonaRes funded by the Federal Ministry of Education and Research, develops strategies to use soil as a sustainable resource for the bio-economy. One part of this initiative is the Intelligence for Soil (I4S) project. It deals with the development of an integrated system for the site-specific soil fertility management. The goal of the project is to establish a mobile sensor platform for soil mapping as well as to apply soil and crop models to deal with the high amount of produced data. These models are going to be used to formulate recommendations on the use of fertilizers and to gain an improvement on the soil fertility. Using this information, the environmental pollution with substances like nitrate contained in fertilizers can be decreased. Furthermore, the costs for farmers can be decreased if only the amounts of fertilizer needed are applied to the field.
The task for BAM is installation and optimization of a handheld XRF sensor for online measurement directly on the field. It is a non-destructive technique and quite suitable for an online approach due to a low maintenance in sample preparation and a fast and simultaneous multi element analysis. The elements of interest in this case are light elements like phosphorus, calcium, and potassium. One of the major tasks is the calibration of the sensor for different soils is finding suitable calibration models, which are evolved to deal with different types of soil. Due to the multi element analysis, micro and macro nutrients can be determined simultaneously. Those differ a lot in their contents, which makes it inevitable to optimize and adapt the analytical procedure. By analyzing soil it is always important to deal with matrix effects, that is why the influence of different disturbances like the moisturize content and the grain size needs to be investigated. The XRF is only one of many sensors that combined give customized fertilizing recommendations in the end.
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.
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils.
Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
Im Rahmen der vom BMBF geförderten Forschungsinitiative BonaRes werden Strategien entwickelt, um Boden als nachhaltige Ressource in der Bioökonomie zu nutzen. Das interdisziplinäre Teilprojekt I4S - „Intelligenz für den Boden“ - ist verantwortlich für die Entwicklung eines integrierten Systems zur standortspezifischen Steuerung der Bodenfruchtbarkeit. Zu diesem Zweck wird eine Plattform gebaut, auf der verschiedene Sensoren installiert sind. Echtzeitdaten werden in Modellen zusammengefasst und Entscheidungsalgorithmen werden verwendet, um die Düngung zu steuern und die Bodenfunktionen entsprechend zu verbessern. Ziel der BAM im Rahmen von I4S ist die Charakterisierung eines energiedispersiven Röntgenfluoreszenzsensors (EDXRF) zur robusten online-Bestimmung von Makro- und Mikronährstoffen im Boden. Zunächst wurde eine Hauptkomponentenanalyse (PCA) durchgeführt, um Ausreißer zu identifizieren und die größte Varianz innerhalb der deutschen Bodenproben zu beobachten. Es konnte festgestellt werden, dass die Aufspaltung der Proben auf ihren Eisengehalt zurückzuführen ist. Da Tonproben hohe Mengen an Eisen und Sandproben geringe Mengen enthalten, war eine Klassifizierung der Proben nach ihrer Bodentextur nach VD LUFA möglich. In Anbetracht der komplexen Bodenzusammensetzung wurde eine matrixspezifische Kalibrierung durch univariate und multivariate Datenanalyse durchgeführt. Die analytischen Güteziffern zeigen, dass ein robusteres Kalibriermodell mit vernachlässigbaren Matrixeffekten durch einen multivariaten Ansatz unter Verwendung der partiellen Regression kleinster Quadrate (PLSR) erhalten werden kann. Verschiedene Faktoren können die erhaltenen Kalibriermodelle beeinflussen, wie z. B. Feuchtigkeit und Partikelgrößenverteilung, was aufgrund der späteren online-Analyse besonders wichtig ist. In ersten Studien wurde der Einfluss von Feuchtigkeit auf den Nährstoffnachweis untersucht. Mit zunehmendem Wassergehalt nehmen die charakteristischen Fluoreszenzpeaks ab und beginnen bei einem Wassergehalt von 15% wieder anzusteigen. Bei geringerem Feuchtigkeitsgehalt agglomeriert der Boden, was zu einer geringeren Packung der Probe führt, dementsprechend zu einer raueren Oberfläche, die die Signale negativ beeinflusst. Bei höherem Wassergehalt bilden sich keine Agglomerate. Dadurch kann die Probe enger gepackt werden, wodurch eine glattere Oberfläche und eine bessere Homogenität erhalten wird. Darüber hinaus führt die Partikelgrößenverteilung beim Vergleich von gemahlenen und nicht gemahlenen Proben zu signifikant höheren Unsicherheiten und niedrigeren Signalen. Beide Faktoren müssen in die chemometrische PLSR einbezogen werden, um zuverlässige Kalibrierungsmodelle für jeden Nährstoff zu erhalten.
Development of a robust calibration model for determination of nutrients in soils using EDXRF
(2018)
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online Analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils. Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
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.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz, die jeweils mit polybromierten Flammschutzmitteln (PBFSM) versehen sind. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Auch soll mit Hilfe der RFA und der LA-ICP-MS die Abreicherung der PBFSM in den Modellmaterialien beschrieben werden. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
Gegenstand der vorzustellenden Arbeiten ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polystyrol (PS) und Polypropylen (PP) zum Einsatz, die jeweils mit polybromierten Flammschutzmitteln (PBFSM) versehen sind. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Auch soll mit Hilfe der RFA und der LA-ICP-MS die Abreicherung der PBFSM in den Modellmaterialien beschrieben werden. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden.
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils.
Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
Influence of moisture and grain sizes on the analysis of nutrients in agricultural soils using LIBS
(2018)
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression(PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Environmental sustainability and –stability of Materials concerning the Migration of pollutants
(2018)
MaUS is an acronym for ”Material und Umweltsimulationen“. Plastics are in the focus of environmental politics due to their long-term behaviour and therefore to their persistence. Not only that they appear as visible contaminants in the sea and on the beach, but their unknown behaviour concerning their additives as well as the related transformation products are anxious. Therefore, we wish to establish a certified reference method to provide a method for testing plastics.
Aim of this project is the development of fast motion standard reference methods for testing plastics regarding to their environmental compatibility. To establish these testing methods, we use polystyrene (PS) and polypropylene (PP) with environmental relevant brominated flame retardants, known for their persistent bioaccumulative and toxic (PBT) properties. In case of PS the material contains 1 wt% of 1,2,5,6,9,10-hexabromocyclododecan (HBCD) and in case of PP 0.1 wt% bromodiphenylether (BDE-209), which is known as a substance of very high concern (SVHC). Furthermore, we use polycarbonate (PC), which is still used as material in baby flasks and releases Bisphenol A (BPA), an estrogenic active substance.
As an additional material PTFE is used for its importance as a source for two ubiquitous environmental substances (PFOS and PFOA), whose toxicological effects are still incompletely known.
The focus in this current work is set on the transfer of potential pollutants out of applied materials mentioned above into environmental compartments like water or soil. Here an accelerated aging concept should be developed to shortened time consuming natural processes. For these resulting simulations we use a programmable weathering chamber with dry and wet periods and with high and low temperatures. These programmes run for several weeks and according to a defined sampling schedule we take water samples, run a clean-up procedure by SPE (Molecular imprinted polymers (MiPs) resp. polymer-based cartridges (Waters Oasis HLB)) and analyse them by HPLC-UV resp. LC-MS/MS. Of most interest in case of flame retardants are photocatalytic transformation products. Therefore, we conduct a non-target-screening resp. a suspected target-screening by LC-MS/MS and HRMS.
Gegenstand des Projekts ist die Prüfung der Umweltbeständigkeit und -verträglichkeit von Materialien und Produkten hinsichtlich der Emission von potenziellen Schadstoffen in die Umwelt. Hierzu werden chemisch-physikalische Einflüsse (Bewitterung) und mikrobielle Beanspruchungen an Modellmaterialien evaluiert. So werden die Freisetzungsraten von Schadstoffen in Abhängigkeit der Beanspruchung beschrieben. Als Modellmaterialien kommen die Polymere Polycarbonat, Polytetrafluorethylen, Polystyrol und Polypropylen zum Einsatz. Synergistische Effekte der Bewitterungsparameter und der mikrobiologischen Beanspruchung sollen dabei ebenso betrachtet werden, wie die gezielte Alterung. Auch findet eine Beschreibung des Verhaltens der ausgetragenen Schadstoffe in den Umweltkompartimenten Boden oder Wasser statt. Hier sind mit Hilfe der zu entwickelnden Screening- und non-Target-Analyseverfahren die Transformation und der Metabolismus durch Mikroorganismen zu beschreiben. Aus den Ergebnissen sollen Korrelationen zwischen den künstlichen Alterungsverfahren und realen Szenarien abgeleitet werden, die es gestatten, Schnellprüfverfahren zu etablieren, die die Simulation der realen Beanspruchungen im Zeitraffermodell anwendbar machen. Somit sollen standardisierbare Schnellbeanspruchungs-verfahren erarbeitet werden, die als Prüfeinrichtungen etabliert werden und von externen Auftraggebern zur Prüfung der Umweltbeständigkeit und -verträglichkeit von neuen Materialien genutzt werden können. Die Umweltwirkungen (chemisch-physikalisch und mikrobiologisch) sollen so definiert eingesetzt werden, dass eine reproduzierbare Prüfung möglich wird. Aus diesen Verfahren und Methoden sollen Normen abgeleitet werden, die eine standardisierte Materialprüfung ermöglichen.
Bei der vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Förderinitiative „BonaRes“ steht die nachhaltige Nutzung der knappen Ressource Boden im Fokus. Das durch BonaRes geförderte Verbundprojekt „Intelligence for Soil“ (I4S) hat sich die Entwicklung eines integrierten Systems zur ortsspezifischen Steuerung der Bodenfruchtbarkeit zur Aufgabe gemacht, welches Empfehlungen zur Anpassung der Düngung und anderer Maßnahmen gibt, um die Bodenfunktionen zu verbessern und Umweltbelastungen zu vermindern. Das System soll aus drei Hauptkomponenten bestehen:
- Mobile Sensorplattform zur hochauflösenden Kartierung der Bodeneigenschaften (BonaRes Mapper). Die Sensorplattform enthält Bodensensoren zur komplexen Erfassung bewirtschaftungsrelevanter Bodeneigenschaften in situ.
- Dynamische Bodenmodelle (Nährstoff-, Wasser-, C-Haushalt) zur Verarbeitung der Sensordaten und Erzeugung entscheidungsrelevanter Informationen,
- Entscheidungsunterstützungssystem (EUS), welches in die Bodenmodelle agronomische und sozioökonomische Randbedingungen integriert, Düngungsempfehlungen ausspricht und Bodenfunktionen bewertet. Der Output des EUS sind insbesondere Düngungskarten für die ortsspezifische Düngung im Rahmen von Precision Agriculture.
Der Erhalt und die Verbesserung der Fruchtbarkeit von Böden durch landwirtschaftliche Bewirtschaftungsmaßnahmen erfordert sorgfältig geplante Entscheidungen, die auf einer detaillierten Erfassung der Bodeneigenschaften und einem vertieften Verständnis der Bodenprozesse beruhen. Durch konventionelle, flächeneinheitliche Bewirtschaftungen (z.B. Düngung) können zum einen Ertragsverluste durch zu geringe Bewirtschaftungsintensität auf dem einen Teil der Fläche verursacht werden, während andere Teile des Feldes zu hohe Dosen erhalten und es dadurch zu Umweltbelastungen oder zu Verschwendung von Ressourcen kommt. Trotz der Verfügbarkeit von Technologien für ortsspezifische Düngung ist die Akzeptanz ortsspezifischer Bewirtschaftung (Precision Agriculture) in der Praxis noch gering. Ein wesentlicher Grund dafür ist das Fehlen von kostengünstigen Methoden zur Erfassung der bewirtschaftungsrelevanten Bodenmerkmale. Für die elementanalytische Untersuchung von Böden vor-Ort ist eine zuverlässige Analytik und Prozessüberwachung wie z.B. durch RFA oder LIBS erforderlich.
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.
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.
The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed.
Für die Erfassung der Verbreitung von Mikroplastik (MP) in der Umwelt ist die zeit- und kostenaufwendige Analysestrategie und der damit verbundene geringe Probendurchsatz eine limitierende Größe. Eine große Zahl verschiedener Studien dokumentriet das Auftreten von MP über den gesamten Globus. Meist sind die Studien aufgrund des großen analytischen Aufwands auf exemplarische, stichpunktartige Untersuchungen kleiner Umweltaliquoten und zahlenmäßig kleiner Probenumfänge begrenzt. Um die Verbreitung, die Eintragspfade und den Verbleib von MP in der Umwelt besser zu verstehen und effektive Vermeidungsstrategien abzuleiten, ist es jedoch notwendig, analytisch mehr Proben erfassen zu können.
Bildgebende mikro-spektroskopische Methoden wie das Raman- und FTIR-Imaging ermöglichen eine zeitaufwendige, umfassende Charakterisierung kleiner Umweltaliquoten. Neben der Partikelanzahl sind zusätzlich Informationen zu Partikelgröße, Größenverteilung und Oberflächenmorphologie zugänglich. Chemische und thermische Extraktionsverfahren sind bereits deutlich schneller und können diese Informationen durch eine Massenbilanz vervollständigen. Die analysierbare Probenmenge ist jedoch auf Milligramm Mengen beschränkt.
Wir schlagen daher vor, die Analyse von Proben auf MP durch ein vorangestelltes Screening mit der Nahinfrarot-Spektroskopie (NIRS) zur komplementieren. In diesem wird bereits eine erste Einschätzung über die Präsenz von MP in einer Probe gefällt und dadurch die wertvolle Messzeit anderer Methoden effizienter genutzt.
NIR zur Analyse von Polymeren wird seit langem eingesetzt, jedoch bisher lediglich im Rahmen einer Studie zur Mikroplastikuntersuchung mittels Hyperspektraler Bildgebung beschrieben. Der NIR Spektralbereich findet sich zwischen dem sichtbaren Licht und dem mittleren Infrarot (MIR). MIR Spektren sind durch klar definierte Banden charakterisiert, welche mehrheitlich von den Grundschwingungen der Moleküle stammen. Die höheren Energien im nahen Infrarot regen hingegen Kombinations- und Oberschwingungen der Streck und Biegeschwingungen an. Die resultierenden Absorptionsbanden sind oft breit und relativ unspezifisch. Erst mit Hilfe einer computergestützten Datenauswertung lassen sich aus diesen Spektren nützliche Informationen gewinnen. Dies erklärt die steigende Popularität der NIR-Spektroskopie in der jüngeren Vergangenheit mit einem Schwerpunkt als prozessanalytische Methode. NIR Spektrometer für das industrielle Prozessmonitoring zeichnen sich durch eine kompakte und robuste Konstruktionsweise aus. Die verfügbaren faseroptischen Reflexionssonden eignen sich gut um pulverförmige Proben zu untersuchen. Der räumlich erfassbare Messbereich kann durch die Sondengeometrie variiert werden. Sind die untersuchten Partikel im Verhältnis zur abgetasteten Fläche klein, wird als spektrale Information die Summe der Absorption aller Partikel im Sichtfeld erfasst. Die Methode ist deshalb nicht für Detailuntersuchungen von MP geeignet, erlaubt es jedoch innerhalb weniger Minuten eine Einschätzung über das Vorkommen von Mikroplastik in einer Probe zu treffen.
Exemplarisch wurden für diese Untersuchungen vier der am weitesten verbreiteten Kunststoffe Polyethylen (PE), Polyethylenterephthalat (PET), Polypropylen (PP) und Polystyrol (PS) gewählt. Aus den additivfreien Polymeren wurden nach einer Kryo-vermahlung und anschließender Siebung (< 125 µm) Modellproben generiert. Die Polymere wurden dafür zu einem Massenanteil von 1 % mit einem Standardboden (LUFA2.3, gesiebt < 125 µm) vermischt. Die Gesamtmenge von 1 g je Probe wurde in Aluminiumbehältern präpariert und 8 Messungen an unterschiedlichen, zufällig gewählten Positionen vorgenommen. Die erhaltenen Spektren wurden zur Kalibrierung chemometrischer Modelle genutzt.
In einem hierarchischen Ansatz wurde anhand der NIR-Spektren eine Klassifizierung vorgenommen:
1. Bestimmung ob eine Probe MP enthält (Ja/Nein).
2. Identifikation der Polymere in der Probe.
Eine aussagekräftige Klassifizierung beruht auf einer Vorbehandlung der Spektren. Hierdurch werden die Unterschiede zwischen den einzelnen Polymerbanden hervorgehoben. Die Eignung der so erstellten Modelle wurde anhand eines Referenzmaterials und am Beispiel von Realproben erfolgreich getestet. Dabei zeigte sich, dass nicht nur in den erstellten Polymer-Bodenmischungen, sondern auch in den Rückständen von fermentiertem Bioabfall und in Filterrückständen einer Waschmaschine, MP richtig erkannt wurde. Weiterhin zeigten Tests mit Mikroplastik-freien Bodenproben unterschiedlicher Herkunft, dass keine falsch-positive Resultate erzeugt wurden. Alle vier untersuchten Polymere, d.h. PE, PET, PS und PP mit einem Massenanteil von 1 % in einer Bodenmatrix werden auch bei einer gemischten Polymerzusammensetzung mit der NIR-Spektroskopie erkannt.
Der kombinierte Einsatz von NIRS und Chemometrie ermöglicht die Entscheidung über ein potenzielles Vorkommen sowie die Zuordnung des Materials der enthaltenen Polymerpartikel für eine Massefraktion ≥ 1 % in einer (trockenen) Probenmenge von 1 g innerhalb von 10–15 min. Der zeitaufwendige Schritt der Methode liegt hier in der Erstellung geeigneter chemometrischer Modelle sowie deren Validierung. Wesentliche Voraussetzung ist dabei, dass bei der Kalibrierung die Varianz der zu erwartenden Partikel und der Matrix realistisch abgebildet wird.
Der Vortrag stellt einige aktuelle Herausforderungen für die Prozessanalytik und mögliche Antworten vor.
Gepulste Raman-Spektrometer akkumulieren das Raman-Signal mit Hilfe schneller optischer Schalter im Picosekunden-Bereich, bevor langlebigere Fluoreszenzanregung entsteht. Damit lassen sich stark fluoreszierende Materialien untersuchen, die bislang nicht zugänglich sind. Eine weitere interessante Entwicklung ist etwa die Shifted excitation Raman difference spectroscopy (SERDS) die besonders für biologische Anwendungen interessant ist.
Flexible, modulare Produktionsanlagen stellen einen vielversprechenden Ansatz für die kontinuierliche Produktion von Fein- und Spezialchemikalien dar. In einem EU-Projekt wurde die Feldintegration eines Online-NMR-Sensormoduls als smartes Modul für die Prozesskontrolle vorangebracht. Dieses Modul basiert auf einem kommerziell erhältlichen Niederfeld-NMR-Spektrometer, welche zurzeit für die Anwendung im Laborbereich erhältlich ist. Für die Feldintegration wurde ein ATEX-zertifiziertes, explosionsgeschütztes Gehäuse entwickelt sowie Automationsschemen für den unbeaufsichtigten Betrieb und für die kalibrierfreie spektrale Datenauswertung erstellt.
Eine sehr gut anwendbare analytische Messtechnik zur Kontrolle der elementaren Zusammensetzung von verschiedensten Materialien ist die laserinduzierte Plasmaspektroskopie (LIPS, engl. LIBS - Laser-induced Breakdown Spectroscopy). Bei der LIBS wird ein kurz gepulster Laser auf eine Probe fokussiert, um ein Leuchtplasma zu erzeugen. Das dabei erzeugte Atomemissionsspektrum ermöglicht eine qualitative und quantitative Analyse der Zusammensetzung der Probe bezüglich praktisch aller Elemente des Periodensystems. In einem aktuellen Projekt wird diese Methode neben anderen zur Online-Analyse von Ackerböden für die ortsspezifischer Bewirtschaftung (Precision Agriculture) weiterentwickelt und bewertet.