Resource-efficient recycling of composites via pyrolysis

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Современные подходы в диагностике и лечении тромбозов системы нижней полой вены

ТРОМБОЗ ВЕНОЗНЫЙ /ДИАГН /ТЕРПОЛАЯ ВЕНА, НИЖНЯЯТРОМБОЭМБОЛИЯЛЕГОЧНАЯ ЭМБОЛИ

Multivariate Analysis of 2D-NMR Spectroscopy : Applications in wood science and metabolomics

Wood is our most important renewable resource. We need better quality and quantity both according to the wood itself and the processes that are using wood as a raw material. Hence, the understanding of the chemical composition of the wood is of high importance. Improved and new methods for analyzing wood are important to achieve better knowledge about both refining processes and raw material. The combination of NMR and multivariate analyses (MVA) is a powerful method for these analyses but so far it has been limited mainly to 1D NMR. In this project, we have developed methods for combining 2D NMR and MVA in both wood analysis and metabolomics. This combination was used to compare samples from normal wood and tension wood, and also trees with a down regulation of a pectin responsible gene. Dissolving pulp was also examined using the same combination of 2D-NMR and MVA, together with FT-IR and solid state 13C CP-MAS NMR. Here we focused on the difference between wood type (softwood and hardwood), process type (sulfite and sulfate) and viscosity. These methods confirmed and added knowledge about the dissolving pulp. Also reactivity was compared in relation to morphology of the cellulose and pulp composition. Based on the method and software used in the wood analysis projects, a new method called HSQC-STOCSY was developed. This method is especially suited for assignment of substances in complex mixtures. Peaks in 2D NMR spectra that correlate between different samples are plotted in correlation plots resembling regular NMR spectra. These correlation plots have great potential in identifying individual components in complex mixtures as shown here in a metabolic data set. This method could potentially also be used in other areas such as drug/target analyses, protein dynamics and assignment of wood spectra

Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Background: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in 1H NMR spectra has previously been successfully employed. Similar correlation of 2D 1H-13C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine). Results: From 50 1H-13C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database. Conclusions: Correlation plots prepared by statistically correlating 1H-13C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.Originally published in manuscript form.</p

Release of carbon nanotubes during combustion of polymer nanocomposites in a pilot-scale facility for waste incineration

Nanocomposites, formed by incorporating nanoparticles into a matrix of standard materials, are increasing on the market. Little focus has been directed towards safe disposal and recycling of these new materials even though the disposal has been identified as a phase of the products' life cycle with a high risk of uncontrolled emissions of nanomaterials. In this study, we investigate if the carbon nanotubes (CNTs), when used as a filler in two types of polymers, are fully destructed in a pilot-scale combustion unit designed to mimic the combustion under waste incineration. The two polymer nanocomposites studied, polycarbonate (PC) with CNT and high-density polyethylene (HDPE) with CNT, were incinerated at two temperatures where the lower temperature just about fulfilled the European waste incineration directive while the upper was chosen to be on the safe side of fulfilling the directive. Particles in the flue gas were sampled and analysed with online and offline instrumentation along with samples of the bottom ash. CNTs could be identified in the flue gas in all experiments, although present to a greater extent when the CNTs were introduced in PC as compared to in HDPE. In the case of using PC as polymer matrix, CNTs were identified in 3–10% of the analysed SEM images while for HDPE in only ~0.5% of the images. In the case of PC, the presence of CNTs decreased with increasing bed temperature (from 10% to 3% of the images). The CNTs identified were always in bundles, often coated with remnants of the polymer, forming particles of ~1–4 μm in diameter. No CNTs were identified in the bottom ash, likely explained by the difference in time when the bottom ash and fly ash are exposed to high temperatures (~hours compared to seconds) in the pilot facility. The results suggest that the residence time of the fly ash in the combustion zone is not long enough to allow full oxidation of the CNTs. Thus, the current regulation on waste incineration (requiring a residence time of the flue gas >850 °C during at least 2 s) may not be enough to obtain complete destruction of CNTs in polymer composites. Since several types of CNTs are known to be toxic, we stress the need for further investigation of the fate and toxicity of CNTs in waste treatment processes