6 Materialchemie
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In this article, we present a novel one-pot mechanochemical reaction for the surface activation of lignin.
The process involves environmentally friendly oxidation with hydrogen peroxide, depolymerization of fractions with high molecular mass, and introduction of new carbonyl functions into the lignin backbone. Kraft lignin was ground with sodium percarbonate and sodium hydroxide in a ball mill at different time intervals. Analyses by infrared spectroscopy (IR), nuclear magnetic resonance spectroscopy (NMR), size exclusion chromatography (SEC), dynamic vapor sorption (DVS), and small-angle X-ray scattering (SAXS) showed significant improvements. After only 5 min of reaction, there was a 47% reduction in mass-average molecular weight and an increase in carboxyl functionalities.
Chemical activation resulted in an approximately 2.8-fold increase in water adsorption. Principal component analysis (PCA) provided further insight into the correlations between IR spectra and SAXS parameters
The structure and composition of polycarbonate polydimethylsiloxane copolymer (PC-co-PDMS) was investigated by applying various analytical approaches including chromatographic separation methods, spectrometric, and spectroscopic detection techniques. In particular, size exclusion chromatography (SEC) and liquid adsorption chromatography operating at different conditions (e.g. using gradient solvent systems) were used to achieve separations according to molar mass and functionality distribution. The coupling of both techniques resulted in fingerprint two-dimensional plots, which could be used to easily compare different copolymer batches. Matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) mass spectrometry was applied for structural investigations. The different ionization behavior of both comonomers, however, strongly limited the applicability of this technique. In contrast to that, Fourier-transform Infrared (FTIR) spectroscopy could be used to quantify the amount of PDMS in the copolymer at different points in the chromatogram. The resulting methodology was capable of distinguishing PC-co-PDMS copolymer from PC homopolymer chains present in the material.
Clays are crucial mineral phases in Earth’s weathering engine, but we do not know how they form in surface environments under (near-)ambient pressures and temperatures. Most synthesis routes, attempting to give insights into the plausible mechanisms, rely on hydrothermal conditions, yet many geological studies showed that clays may actually form at moderate temperatures (<100 °C) in most terrestrial settings. Here, we combined high-energy X-ray diffraction, infrared spectroscopy, and transmission electron microscopy to derive the mechanistic pathways of the low-temperature (25–95 °C) crystallization of a synthetic Mg-clay, saponite. Our results reveal that saponite crystallizes via a two stage process: (1) a rapid (several minutes) coprecipitation where ∼20% of the available magnesium becomes incorporated into an aluminosilicate network, followed by (2) a much slower crystallization mechanism (several hours to days) where the remaining magnesium becomes gradually incorporated into the growing saponite sheet structure.
The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters.
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.
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.
New Focus On Boehmite-Reinforced Nanocomposites Molecular Approach With Advanced FTIR-Techniques
(2019)
By FTIR-study it was possible to proof a chemical reaction between boehmite and the hardener of anhydride cured epoxy resins. Future studies can assume that the chemical environment of the resin system is changed in the surrounding of boehmite nanoparticles. This highly affects especially localized properties.
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.