Analytische Chemie
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The increasing pollution of terrestrial and aquatic ecosystems with plastic debris, which leads to the accumulation of microscopic plastic particles of still unknown fate, is an upcoming problem of our time. In order to monitor the degree of contamination and to understand the underlying processes of degradation and internalization of plastic debris, analytical methods are urgently needed, which help to identify and quantify microplastics. Currently, expensive collected and purified materials enriched on filters are investigated by (micro) infrared spectroscopy (FTIR). Few studies using micro-Raman spectroscopy have been published as well. In contrast to FTIR, Raman spectroscopy can handle wet samples, but it suffers from interference of fluorescent materials. Both micro-FTIR- and micro-Raman, always include time consuming scanning and mapping procedures followed by the manual inspection and measurement of selected particles.
The TED-GC-MS analysis is a two-step method. A sample is first decomposed in a thermogravimetric analyzer (TGA) and the gaseous decomposition products are then trapped on a solid-phase adsorber. Subsequently, the solid-phase adsorber is analyzed with thermal desorption gas chromatography mass spectrometry (TDU-GC-MS). This method is ideally suited for the analysis of polymers and their degradation processes. Here, a new entirely automated System is introduced which enables high sample throughput and reproducible automated fractioned collection of decomposition products. Strengths and limitations of the system configuration are elaborated via three examples focused on practical challenges in materials analysis and identification: i) separate analysis of the components of a wood-plastic-composite material, ii) quantitative determination of weight concentration of the constituents of a polymer blend and iii) quantitative analysis of model samples of microplastics in suspended particulate matter.
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.
In this study, a method for the determination of tire and road wear particle (TRWP) contents in particulate samples from road Environment was developed. Zn was identified as the most suitable elemental marker for TRWP, due to its high concentration in tire tread and the possibility of separation from other Zn sources. The mean concentration of 21 tire samples was 8.7 ± 2.0 mg Zn/g. Before quantification in samples from road environment, TRWP were separated from the particulate matrix by density separation. Method development was conducted using shredded tread particles (TP) as a surrogate for TRWP.
Recovery of TP from spiked sediment was 95 ± 17% in a concentration range of 2 - 200 mg TP/g. TP determination was not affected by other Zn containing solids or spiked Zn-salts. By adjusting the density of the separation solution to 1.9 g/cm3, more than 90% of total TRWP were separated from the sample matrix. TRWP concentrations in particulate matter collected in two road runoff treatment Systems ranged from 0.38 to 150 mg TRWP/g. Differences in quantified TRWP contents of the two Systems indicate changes in particle dynamics due to ageing and aggregation processes. The developed method allows TRWP determination in road runoff and in environments that are influenced by road traffic. The validated separation procedure can also be applied for TRWP characterization in future studies.
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.
In the last years, the Process Analytical Technology (PAT) research field has become of great importance due to its connection with the industrial world. This industry related research, along with the evolutionary progress that technology has seen lately, has allowed that key analytical techniques in an industrial environment have undergone a radical development. One of these techniques has been the near-infrared spectroscopy (NIRS). This technique, currently common in "online" industrial analysis, has seen a remarkable revolution, especially since the introduction of the microelectromechanical systems technology (MEMS) in the spectroscopy field. Nowadays it is possible to find compact spectrometers no bigger than a wristwatch in the market. Although there is an unavoidable question: can these compact spectrometers actually compete against the traditional spectrometers?
In this project different calibration parameters of two compact NIR spectrometers ("Spectral Engines Oy NIR-One Sensor NM2.0", with 1550 to 1950 nm range; and "NeoSpectra Si-ware", with 1300 to 2550 nm range) were evaluated, and the obtained results were compared with a reference spectrometer ("Bruker Optik GmbH Matrix-F", with 15000 to 4000 cm–1 range). In order to obtain the different calibration parameters, a sequence of quality performance tests were conducted. The results obtained after the different experiments carried out with both compact spectrometers prove that their performance is more than acceptable for routine analysis.
Afterward, model samples of different microplastics in soil at different known concentration were analyzed with all three spectrometers. Chemometric models capable to identify and classify microplastics in soil were established. For this analysis five of the most used plastics worldwide were used: polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). After the Principal component analysis (PCA), it can be seen that only the NIR-One NM2.0 is capable to differentiate all types of microplastics in soil at concentrations of 1–2 %, while the NeoSpectra Si-ware is unable to identify the PET sample.
A harmonization of sampling, sample preparation and detection is pivotal in order to obtain comparable data on microplastics (MP) in the environment. This paper develops and proposes a suitable sampling concept for waterbodies that considers different plastic specific properties and influencing factors in the environment.
Both artificial water including defined MP fractions and the discharge of a wastewater treatment plant were used to verify the derived sampling procedure, sample preparation and the subsequent analysis of MP using thermal extraction-desorption gas chromatography - mass spectrometry (TED-GC-MS).
A major finding of this paper is that an application of various particle size classes greatly improves the practical handling of the sampling equipment. Size classes also enable the TED-GC-MS to provide any data on the MP size distribution, a substantial sampling property affecting both the necessary sampling volume and the optimal sampling depth.
In the artificial water with defined MP fractions, the recovery rates ranged from 80 to 110%, depending on the different MP types and MP size classes. In the treated wastewater, we found both Polyethylene and polystyrene in different size classes and quantities.