Capabilities of simultaneous 193 nm - LIBS/LA-ICP-MS imaging for microplastics characterization.

Microplastics (MPs) are currently one of the major environmental challenges within our society. With the awareness of the impact of MPs on the environment increasing over the last years, the need for increased monitoring as well as comprehensive analysis to better understand the fate and impact of MPs has become more and more important. A major aspect of MP characterization is the assignment of the polymer type of individual particles. Here, per- and poly-fluoroalkyl substances (PFAS), originating from fluor-containing polymers, have gained a lot of attention due to the severe environmental impact. Additionally, quantitative analysis of the metal content is of great interest in the field, since MPs are prone to either leaching (in)organic additives into the environment or taking up and accumulating hazardous components (e.g., heavy metals). In this work we demonstrate the capabilities of a simultaneous LIBS/LA-ICP-MS setup for the analysis of MPs. In the first part, we demonstrate the potential of targeted LIBS analysis for the imaging of fluor-containing polymers. Using a laser spot size of 5 µm combined with highly sensitive ICCD detection enables analysis of particles in the low µm range. In the second part we combine the polymer-identification capabilities of LIBS with the high sensitivity of ICP-MS to perform matrix-matched quantification of the metal content of individual MPs. In this case we use a spot size of 50 µm facilitating polymer classification with a broadband spectrometer, resulting in detection limits of 0.72 µg/g for Pb and 9.5 µg/g for Sn simultaneously measured using ICP-MS.

Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition.

The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs matrix-matched external calibration supported by a pixel-by-pixel automatic matrix (tissue type) recognition performed by linear discriminant analysis of the spatially resolved LIBS hyperspectral data set. On a swine (porcine) brain sample, we successfully performed this matrix recognition with an accuracy of 98% for the grey and white matter and we converted a LIBS intensity map of a tissue sample to a correct concentration map for the elements Na, K and Mg. Found concentrations in the grey and white matter agreed the element concentrations published in the literature and our reference measurements. Our results revealed that the actual concentration distribution in tissues can be quite different from what is suggested by the LIBS signal intensity map, therefore this conversion is always suggested to be performed if an accurate concentration distribution is to be assessed.

A versatile approach for the preparation of matrix-matched standards for LA-ICP-MS analysis - Standard addition by the spraying of liquid standards.

In the last years, LA-ICP-MS has become an attractive technique for analyzing solid samples from various research fields. However, application in material science is often hampered by the limited availability of appropriate certified reference materials, which are a precondition for accurate quantification. Thus, frequently in-house prepared standards are used, which match the sample's composition and include all elements of interest at the required concentration levels. However, preparing and characterizing such standards is often labor-intensive and time-consuming. This work proposes a new approach for the fabrication of matrix-matched standards based on the concept of standard addition. In the first step, the analytes of interest are homogeneously deposited onto the sample surface using liquid standards and a spraying device. For analysis, the generated thin layer is ablated simultaneously with the underlying sample. Thereby deviations in the ablation process and particle transport can be avoided. It could be shown that the developed method is highly versatile and could be easily adapted to the actual needs. Using silicon, silicon carbide, copper, aluminum, and glass as a matrix, excellent linear correlations between observed signal intensities and deposited amounts were found for the elements Zn, Ag, In, and Pb (R2 - values greater than 0.99). The method was applied to determine the content of sulfur, zinc, silver, indium, and lead in a commercial Kapton® polyimide film. The obtained results could be verified based on the homogeneously distributed sulfur by conventional liquid ICP-MS analysis after sample digestion, showing similar precision and accuracy. Lead was found to show a very inhomogeneous distribution in the Kapton® film, with concentration below the LOD at most measured locations and irregularly occurring spots with significantly higher concentrations. Finally, a quantitative depth profile of sulfur in a Kapton® film has been measured to assess the uptake of SO2 after a weathering experiment.

Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm.

Microplastics found in the environment are often covered with a biofilm, which makes their analysis difficult. Therefore, the biofilm is usually removed before analysis, which may affect the microplastic particles or lead to their loss during the procedure. In this work, we used laser-based analytical techniques and evaluated their performance in detecting, characterizing, and classifying pristine and aged microplastics with a developed biofilm. Five types of microplastics from different polymers were selected (polyamide, polyethylene, polyethylene terephthalate, polypropylene, and polyvinyl chloride) and aged under controlled conditions in freshwater and wastewater. The development of biofilm and the changes in the properties of the microplastic were evaluated. The pristine and aged microplastics were characterized by standard methods (e.g., optical and scanning electron microscopy, and Raman spectroscopy), and then laser-induced breakdown spectroscopy (LIBS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used. The results show that LIBS could identify different types of plastics regardless of the ageing and major biotic elements of the biofilm layer. LA-ICP-MS showed a high sensitivity to metals, which can be used as markers for various plastics. In addition, LA-ICP-MS can be employed in studies to monitor the adsorption and desorption (leaching) of metals during the ageing of microplastics. The use of these laser-based analytical techniques was found to be beneficial in the study of environmentally relevant microplastics.

Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics.

Over the past few years, laser-induced breakdown spectroscopy (LIBS) has earned a lot of attention in the field of online polymer identification. Unlike the well-established near-infrared spectroscopy (NIR), LIBS analysis is not limited by the sample thickness or color and therefore seems to be a promising candidate for this task. Nevertheless, the similar elemental composition of most polymers results in high similarity of their LIBS spectra, which makes their discrimination challenging. To address this problem, we developed a novel chemometric strategy based on a systematic optimization of two factors influencing the discrimination ability: the set of experimental conditions (laser energy, gate delay, and atmosphere) employed for the LIBS analysis and the set of spectral variables used as a basis for the polymer discrimination. In the process, a novel concept of spectral descriptors was used to extract chemically relevant information from the polymer spectra, cluster purity based on the k-nearest neighbors (k-NN) was established as a suitable tool for monitoring the extent of cluster overlaps and an in-house designed random forest (RDF) experiment combined with a cluster purity-governed forward selection algorithm was employed to identify spectral variables with the greatest relevance for polymer identification. Using this approach, it was possible to discriminate among 20 virgin polymer types, which is the highest number reported in the literature so far. Additionally, using the optimized experimental conditions and data evaluation, robust discrimination performance could be achieved even with polymer samples containing carbon black or other common additives, which hints at an applicability of the developed approach to real-life samples.

Combined LA-ICP-MS/LIBS: powerful analytical tools for the investigation of polymer alteration after treatment under corrosive conditions.

Polymers are used in a variety of different areas, including applications in food packaging, automotive and the semiconductor industry. Information about degradation of these materials during application, but also uptake of pollutants from the surrounding environment is therefore of great interest. Conventional techniques used for polymer characterization such as FT-IR or Raman spectroscopy, but also thermo-analytical techniques offer insights into degradation processes but lack the possibility to detect uptake of inorganic species. Moreover, these techniques do not allow the measurement of depth profiles, thus information about degradation or pollutant uptake with sample depth is not accessible. In this work, we propose LA-ICP-MS and LIBS as powerful analytical tools for polymer characterization, overcoming the limitations of conventional analytical techniques used for polymer analysis. Applicability of the developed procedures is demonstrated by the analysis of artificially weathered polyimides and modern art materials, indicating that the degradation of the polymer but also the uptake of corrosive gases is not limited to the sample surface. Finally, a tandem LA-ICP-MS/LIBS approach is employed, which combines the advantages of both laser-based procedures, enabling the simultaneous analysis of polymer degradation and cadmium uptake of polystyrene after exposure to UV radiation and treatment with artificial sea water.

Multivariate analysis and laser-induced breakdown spectroscopy (LIBS): a new approach for the spatially resolved classification of modern art materials.

The ever-increasing speed of exchange of ideas, information, and culture allows contemporary art to be in constant growth, especially concerning the choice of artistic materials. Their characterization is not only crucial for the study of artistic techniques but also for research into the stability of the material and, consequently, the best preservation practices. For this aim, an analytical method should have the advantages of not requiring sample preparation, performing superficial micro-analysis, and obtaining detailed spectral information. For this study, laser-induced breakdown spectroscopy (LIBS) was employed. It was used for the identification of modern paints composed of inorganic pigments and organic binders, such as acrylics, alkyds, and styrene-acrylics. Principal component analysis (PCA) was used to classify the different pure materials, above all, the polymeric binders. To distinguish the paint mixtures, whose LIBS spectral results were more complex due to the pigment/binder interaction, a statistical method recently employed in the cultural heritage field was chosen, namely, random decision forest (RDF). This methodology allows a reduction of the variance of the data, testing of different training data sets by cross-validation, an increase of the predictive power. Furthermore, for the first time, the distribution of different inorganic pigments and organic binder materials in an unknown sample was mapped and correctly classified using the developed RDF. This study represents the first approach for the classification of modern and contemporary materials using LIBS combined with two different multivariate analyses. Subsequent optimization of measurement parameters and data processing will be considered in order to extend its employment to other artistic materials and conservation treatments.

Spatially resolved polymer classification using laser induced breakdown spectroscopy (LIBS) and multivariate statistics.

Synthetic polymers and plastics have become one of the most important materials in our modern world and everyday life with all kinds of applications mainly due to their wide range of excellent and tuneable properties. Several novel materials consisting of multiple different synthetic polymers or composite materials like natural-fiber-reinforced polymer composites have already been reported in literature. Additionally, materials consisting of multiple synthetic polymers already found their way in our daily lives (e.g. double-sided adhesive tape). With emerging materials consisting of different structured synthetic polymers, the need for analytical methods characterizing these kinds of sample also arises. Conventionally, analytical techniques such as FT-IR or Raman spectroscopy are used for polymer classification. Although, these techniques offer laterally resolved investigations they lack the possibility of analyzing depth profiles. In this work, we present laser induced breakdown spectroscopy (LIBS) as a novel and powerful analytical method for spatially resolved polymer classification. As a feasibility study, two exemplary structured synthetic polymer samples (2D structured and multilayer system) have been analyzed using LIBS and the spatial distribution of 5 different synthetic polymers, namely acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene (PE), polyacrylate (PAK) and polyvinylchloride (PVC) have been successfully classified using multivariate statistical approaches (principal component analysis (PCA) and k-means clustering). Spatially resolved classification results were validated by comparing the obtained distribution of the 2D structured sample to the elemental distribution of a contamination present in one of the synthetic polymers. Classification of the polymeric multilayer system was validated by comparing the obtained results to a microscopic cross-section. It was shown that LIBS cannot only be used to investigate 2D structured polymer samples but also for direct analysis of depth profiles. Besides synthetic polymer classification, LIBS provides simultaneous analysis of the elemental composition of the sample, which increases the total amount of information accessible with only one measurement.