Laterally resolved chemical analysis (chemical imaging) has increasingly attracted attention in the Life Sciences during the past years. While some developments have provided improvements in lateral ...resolution and speed of analysis, there is a trend toward the combination of two or more analysis techniques, so-called multisensor imaging, for providing deeper information into the biochemical processes within one sample. In this work, a human malignant pleural mesothelioma sample from a patient treated with cisplatin as a cytostatic agent has been analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). While LA-ICPMS was able to provide quantitative information on the platinum distribution along with the distribution of other elemental analytes in the tissue sample, MALDI MS could reveal full information on lipid distributions, as both modes of polarity, negative and positive, were used for measurements. Tandem MS experiments verified the occurrence of distinct lipid classes. All imaging analyses were performed using a lateral resolution of 40 μm, providing information with excellent depth of details. By analyzing the very same tissue section, it was possible to perfectly correlate the obtained analyte distribution information in an evaluation approach comprising LA-ICPMS and MALDI MS data. Correlations between platinum, phosphorus, and lipid distributions were found by the use of advanced statistics. The present proof-of-principle study demonstrates the benefit of data combination for outcomes beyond one method imaging modality and highlights the value of advanced chemical imaging in the Life Sciences.
Microplastics (MP) have been detected in bottled mineral water across the world. Because only few MP particles have been reported in ground water-sourced drinking water, it is suspected that MP enter ...the water during bottle cleaning and filling. However, until today, MP entry paths were not revealed. For the first time, this study provides findings of MP from the well to the bottle including the bottle washing process. At four mineral water bottlers, five sample types were taken along the process: raw and deferrized water samples were filtered in situ; clean bottles were sampled right after they left the bottle washer and after filling and capping. Caustic cleaning solutions were sampled from bottle washers and MP particles isolated through enzymatic and chemical treatments. The samples were analyzed for eleven synthetic and natural polymer particles ≥11 µm with Fourier-transform infrared imaging and random decision forests. MP were present in all steps of mineral water bottling, with a sharp increase from <1 MP L−1 to 317 ± 257 MP L−1 attributed to bottle capping. As 81% of MP resembled the PE-based cap sealing material, abrasion from the sealings was identified as the main entry path for MP into bottled mineral water.
Atmospheric aerosol nanoparticles play a major role in many atmospheric processes and in particular in the global climate system. Understanding their formation by homogeneous or heterogeneous ...nucleation as well as their photochemical aging and atmospheric transformation is of utmost importance to evaluate their impact on atmospheric phenomena. Single particle analysis like tip-enhanced Raman spectroscopy (TERS) opens access to a deeper understanding of these nanoparticles. Atmospherically relevant nanoparticles, formed above a simulated salt lake inside an aerosol smog-chamber, were analyzed using TERS. TERS spectra of 11 nanoparticles were studied in detail. First results of TERS on atmospherically relevant aerosol nanoparticles reveal the presence of inorganic seed particles, a chemical diversity of equally sized particles in the nucleation mode, and chemical transformation during photochemical aging. Therefore, single particle analysis by optical near-field spectroscopy such as TERS of atmospheric nanoparticles will significantly contribute to elucidate atmospheric nucleation, photochemical aging, and chemical transformation processes by uncovering single particle based properties.
The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on ...aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 μm2. The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.
A common trait of the more established clustering algorithms such as K-Means and HCA is their tendency to focus mainly on the bulk features of the data which causes minor features to be attributed to ...larger clusters. For hyperspectral imaging this has the consequence that substances which are covered by only a few pixels tend to be overlooked and thus cannot be separated. If small lateral features such as particles are the research objective this might be the reason why cluster analysis fails. Therefore we propose a novel graph-based clustering algorithm dubbed GBCC which is sensitive to small variations in data density and scales its clusters according to the underlying structures. The analysis of the proposed method covers a comparison to K-Means, DBSCAN and KNSC using a 2D artificial dataset. Further the method is evaluated on a multisensor image of atmospheric particulate matter composed of Raman and EDX data as well as an FTIR image of microplastics.
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•Sensitive to small variations in data density.•Complementary clustering approach to K-Means or HCA.•Applicable to complex problems where a large number of clusters is expected.
Chemical imaging is a powerful tool for understanding the chemical composition and nature of heterogeneous samples. Recent developments in elemental, vibrational, and mass-spectrometric chemical ...imaging with high spatial resolution (50-200 nm) and reasonable timescale (a few hours) are capable of providing complementary chemical information about various samples. However, a single technique is insufficient to provide a comprehensive understanding of chemically complex materials. For bulk samples, the combination of different analytical methods and the application of statistical methods for extracting correlated information across different techniques is a well-established and powerful concept. However, combined multivariate analytics of chemical images obtained via different imaging techniques is still in its infancy, hampered by a lack of analytical methodologies for data fusion and analysis. This study demonstrates the application of multivariate statistics to chemical images taken from the same sample via various methods to assist in chemical structure determination.
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.
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The growing importance of fluoropolymers in high-tech applications and green technologies results in the rising need for their characterization. In contrast to conventional methods used for this ...task, laser-induced breakdown spectroscopy (LIBS) provides the advantage of a spatially resolved analysis. Nevertheless, the high excitation energy of fluorine results in low sensitivity of the atomic F(I) lines, which limits the feasibility of its LIBS-based analysis. This work presents a novel approach for quantitative mapping of fluorine in fluoropolymer samples. It bases on monitoring of molecular emission bands (CuF or CaF) arising from fluorine containing molecules. These species were generated during later stages of the LIBS plasma by a recombination of fluorine atoms originating from fluoropolymer sample with a molecule-forming partner (Cu or Ca) stemming from a surface coating. This approach enables F detection limits in the parts per million (μg g−1) range and elemental imaging using single shot measurements. The elements required for molecular formation are deposited on the sample surface prior to analysis. We evaluate two techniques - spray coating and sputter coating – with regards to their effects on sensitivity and spatial resolution in elemental mapping. Overall, both methods proved to be suitable for a spatially resolved analysis of fluorine: whereas sputter-coating of copper yielded a better sensitivity, spray coating of calcium provided a higher spatial resolution.
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•Sputtering of Cu and spray coating of Ca-acetate, were tested to deposit a molecular forming element on the surface of a fluoropolymer.•Reduction of laser energy by a factor of 4 without loss in sensitivity (LOD about 160 μg g−1) compared to atomic emission.•Spatially resolved measurements of fluorine distribution via molecular LIBS possible.
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