Andrea Paul

Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS

AbstractThe authenticity of objects and artifacts is often the focus of forensic analytic chemistry. In document fraud cases, the most important objective is to determine the origin of a particular ink. Here, we introduce a new approach which utilizes the combination of two analytical methods, namely Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The methods provide complementary information on both molecular and elemental composition of samples. The potential of this hyphenation of spectroscopic methods is demonstrated for ten blue and black ink samples on white paper. LIBS and Raman spectra from different inks were fused into a single data matrix, and the number of different groups of inks was determined through multivariate analysis, i.e., principal component analysis, soft independent modelling of class analogy, partial least-squares discriminant analysis, and support vector machine. In all cases, the results obtained with the combined LIBS and Raman spectra were found to be superior to those obtained with the individual Raman or LIBS data sets. FigureCombination of Raman spectroscopy and LIBS for improved classification of inks: score plot from PCA and experimental set-up

Chemical and morphological changes in hydrochars derived from microcrystalline cellulose and investigated by chromatographic, spectroscopic and adsorption techniques.

Hydrothermal carbonization (HTC) can be used for converting the biomass into a carbon-rich material, whose application as a fuel requires higher heating value, whereas soil amendment needs stable carbon. This work was focused on the characterization of hydrochars derived from microcrystalline cellulose. The chars were investigated using elemental analysis, Brunauer-Emmett-Teller technique, nuclear magnetic resonance spectroscopy, Raman, Fourier transform infrared, and electron spin resonance spectroscopy. Severity in temperature between 230 and 270°C with reaction times between 2 and 10 h only affect the carbon content moderately. The results show that aromatization of HTC chars correlates well with temperature, which was further supported by the increase of organic radicals with decreasing g values at higher temperatures. Based on these results, the energetic use of chars favors mild HTC (T<230°C and t≤6 h), while the soil amendement favors serve conditions (T≥230°C, and t>6 h).

Automated data evaluation and modelling of simultaneous 19F–1H medium-resolution NMR spectra for online reaction monitoring

Medium‐resolution nuclear magnetic resonance spectroscopy (MR‐NMR) currently develops to an important analytical tool for both quality control and process monitoring. In contrast to high‐resolution online NMR (HR‐NMR), MR‐NMR can be operated under rough environmental conditions. A continuous re‐circulating stream of reaction mixture from the reaction vessel to the NMR spectrometer enables a non‐invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2‐trifluoroethanol with acetic acid to 2,2,2‐trifluoroethyl acetate both by 1H HR‐NMR (500 MHz) and 1H and 19F MR‐NMR (43 MHz) as a model system. The parallel online measurement is realised by splitting the flow, which allows the adjustment of quantitative and independent flow rates, both in the HR‐NMR probe as well as in the MR‐NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR‐MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR‐NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS‐R). To assess the potential of these evaluation strategies for MR‐NMR, prediction results are compared with the line fitting data derived from the quantitative HR‐NMR spectroscopy. Although, superior results are obtained from both IHM and PLS‐R for 1H MR‐NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment. Copyright

A First Pilot Study on the Sorption of Environmental Pollutants on Various Microplastic Materials

With the drastic increase in plastic production, the input of plastic particles into the environment has become a recognised problem. Xenobiotics are able to sorb to polymer materials, and this process is further enhanced where they encounter microplastics (plastic fragments <5 mm). In this work we studied the sorption of metformin, a type-2 diabetes drug, and difenoconazole, a fungicide, onto the virgin polymer materials polyamide (PA), polypropylene (PP), and polystyrene (PS). Additionally, PP was cryo-milled and PA was treated with acid to investigate the influence of an increase in surface area and chemical modification. The material properties were also studied by dynamic scanning calorimetry (DSC), gel permeation chromatography (GPC) and Fourier transform infrared spectroscopy (FTIR). Sorption experiments were performed on the basis of a full factorial design examining the effect of agitation, pH value, and salinity. Experimental results showed that difenoconazole sorbs readily to all microplastics, whereas the more polar analyte metformin did not show any affinity to the materials used. For difenoconazole the governing factor in all cases is agitation, while both pH and salinity exhibited only a slight influence. The modification of polymers leads to enhanced sorption, indicating that an increase in surface area (cryo-milled PP) or inner volume (acid-treated PA) strongly favours adsorption. Moreover, long-term experiments demonstrated that the time until equilibrium is reached depends strongly on the particle size.

Comparison of time-gated surface-enhanced Raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time‐gated Raman (TG‐Raman)‐, continuous wave NIR‐process Raman (NIR‐Raman), and continuous wave micro‐Raman (micro‐Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell‐free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off‐line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG‐Raman were comparable to those of micro‐Raman resulting in well‐discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR‐Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis.

Mathematical and statistical tools for online NMR spectroscopy in chemical processes

Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which are calibration intensive. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environments for advanced process monitoring and control. Within the European Union’s Research Project CONSENS (Integrated CONtrol and SENsing, www.consens-spire.eu) by development and integration of a smart NMR module for process monitoring was designed and delivers online spectra of various reactions. The presented NMR module is provided in an explosion proof housing of 57 x 57 x 85 cm module size and involves a compact spectrometer together with an acquisition unit and a programmable logic controller for automated data preparation (phasing, baseline correction), and evaluation. For reaction monitoring and process control using NMR instruments after acquisition of the FID the data needs to be corrected in real-time for common effects using fast interfaces and automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. Additionally, the multiplet structure becomes more dominant because of the comparably low-field strengths which results in overlapping of multiple signals. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants, or derivatives and robust automation schemes.