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The characterization of technical lignins is a key step for the efficient use and processing of this material into valuable chemicals and for quality control. In this study 31 lignin samples were prepared from different biomass sources (hardwood, softwood, straw, grass) and different pulping processes (sulfite, Kraft, organosolv). Each lignin was analysed by attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy. Statistical analysis of the ATR-FT-IR spectra by means of principal component analysis (PCA) showed significant differences between the lignins. Hence, the samples can be separated by PCA according to the original biomass. The differences observed in the ATR-FT-IR spectra result primarily from the relative ratios of the p-hydroxyphenyl, guaiacyl and syringyl units. Only limited influence of the pulping process is reflected by the spectral data. The spectra do not differ between samples processed by Kraft or organosolv processes. Lignosulfonates are clearly distinguishable by ATR-FT-IR from the other samples. For the classification a model was created using the k-nearest neighbor (k NN) algorithm. Different data pretreatment steps were compared for k=1…20. For validation purposes, a 5-fold cross-validation was chosen and the different quality criteria Accuracy (Acc), Error Rate (Err), Sensitivity (TPR) and specificity (TNR) were introduced. The optimized model for k=4 gives values for Acc = 98.9 %, Err = 1.1 %, TPR = 99.2 % and TNR = 99.6 %.
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
Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) is a well-implemented analytical technique for the investigation of complex biological samples. In MS, the sample preparation strategy is decisive for the success of the measurements. Here, sample preparation processes and target materials for the investigation of different pollen grains are compared. A reduced and optimized sample preparation process prior to MALDI-TOF measurement is presented using conductive carbon tape as target. The application of conductive tape yields in enhanced absolute signal intensities and mass spectral pattern information, which leads to a clear separation in subsequent pattern analysis. The results will be used to improve the taxonomic differentiation and identification, and might be useful for the development of a simple routine method to identify pollen based on mass spectrometry.
Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) are frequently used for analytical purposes in research and industry, but they seldom are part of the chemistry Curriculum or laboratory exercises. This case study paper describes the combined application of LIBS and PCA during a research internship for an undergraduate student. The instructional method applied was based on a one-on-one mentorship, in which case the learner was engaged in a Research work. The learning activities included theoretical introductions to the LIBS and PCA methods, numerical simulation, experiments, and data analysis.
The study covered three main topics: analysis of LIBS spectra, application of PCA for clustering, and use of PCA for experimental design. The realization of the study was instructive for all parties involved: from the mentorship point of view, it is concluded that the topics can be covered during an internship or developed into a one semester long research-based module of a chemistry program or a final year project. The student, on the other hand, developed profound technical skills in performing experiments and using PCA software for data analysis.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.