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Sulphurous additives for polystyrene: Influencing decomposition behavior in the condensed phase
(2015)
The thermal decomposition behaviour of polystyrene (PS) containing sulphur and phosphorus additives was investigated, using thermogravimetry coupled with Fourier transform infrared spectroscopy (TGA-FTIR). It was found that the additives influence the decomposition process of the polymer in the condensed phase, resulting in a decrease in styrene monomer formation and an increase in styrene oligmer derivatives. Via reference measurements with binary mixtures it was found that the presence of sulphur additives influences the radicalic decomposition process of PS. In combination with quantum chemical calculations it was concluded that this is due to the formation of radicals that abstract hydrogen from the polymer matrix at lower temperatures, disfavouring the radicalic decomposition pathway leading to styrene.
The improved Monte-Carlo (MC) method for standard-less analysis in laser induced breakdown spectroscopy (LIBS) is presented. Concentrations in MC LIBS are found by fitting model-generated synthetic spectra to experimental spectra. The current version of MC LIBS is based on the graphic processing unit (GPU) computation and reduces the analysis time down to several seconds per spectrum/sample. The previous version of MC LIBS which was based on the central processing unit (CPU) computation requested unacceptably long analysis times of 10's minutes per spectrum/sample. The reduction of the computational time is achieved through the massively parallel computing on the GPU which embeds thousands of co-processors. It is shown that the number of iterations on the GPU exceeds that on the CPU by a factor > 1000 for the 5-dimentional parameter space and yet requires > 10-fold shorter computational time. The improved GPU-MC LIBS outperforms the CPU-MS LIBS in terms of accuracy, precision, and analysis time. The performance is tested on LIBS-spectra obtained from pelletized powders of metal oxides consisting of CaO, Fe2O3, MgO, and TiO2 that simulated by-products of steel industry, steel slags. It is demonstrated that GPU-based MC LIBS is capable of rapid multi-element analysis with relative error between 1 and 10's percent that is sufficient for industrial applications (e.g. steel slag analysis). The results of the improved GPU-based MC LIBS are positively compared to that of the CPU-based MC LIBS as well as to the results of the standard calibration-free (CF) LIBS based on the Boltzmann plot method.
Drug and gene delivery via nanoparticles across biological barriers such as the alveolar-capillary barrier of the lung constitutes an interesting and increasingly relevant field in nanomedicine. Nevertheless, potential hazardous effects of nanoparticles (NPs) as well as their cellular and systemic fate should be thoroughly examined. Hence, this study was designed to evaluate the effects of amorphous silica NPs (Sicastar) and (poly)organosiloxane NPs (AmOrSil) on the viability and the inflammatory response as well as on the cellular uptake mechanisms and fate in cells of the alveolar barrier. For this purpose, the alveolar epithelial cell line (NCI H441) and microvascular endothelial cell line (ISO-HAS-1) were used in an experimental set up resembling the alveolar-capillary barrier of the lung. In terms of IL-8 and sICAM Sicastar resulted in harmful effects at higher concentrations (60 µg/ml) in conventional monocultures but not in the coculture, whereas AmOrSil showed no significant effects. Immunofluorescence counterstaining of endosomal structures in NP-incubated cells showed no evidence for a clathrin- or caveolae-mediated uptake mechanism. However, NPs were enclosed in flotillin-1 and -2 marked vesicles in both cell types. Flotillins appear to play a role in cellular uptake or trafficking mechanisms of NPs and are discussed as indicators for clathrin- or caveolae-independent uptake mechanisms. In addition, we examined the transport of NPs across this in vitro model of the alveolar-capillary barrier forming a tight barrier with a transepithelial electrical resistance of 560 ± 8 Ω cm². H441 in coculture with endothelial cells took up much less NPs compared to monocultures. Moreover, coculturing prevented the transport of NP from the epithelial compartment to the endothelial layer on the bottom of the filter insert. This supports the relevance of coculture models, which favour a differentiated and polarised epithelial layer as in vitro test systems for nanoparticle uptake.
Tomography of a laser-induced plasma in air is performed by inverse Radon transform of angle-resolved plasma images. Plasmas were induced by single laser pulses (SP), double pulses (DP) in collinear geometry, and by a combination of single laser pulses with pulsed arc discharges (SP-AD). Images of plasmas on metallurgical steel slags were taken at delay times suitable for calibration-free laser-induced breakdown spectroscopy (CFLIBS). Delays ranged from few microseconds for SP and DP up to tens of microseconds for SP-AD excitation. The white-light and the spectrally resolved emissivity ε(x,y,z) was reconstructed for the three plasma excitation schemes. The electron number density Ne(x,y,z) and plasma temperature Te(x,y,z) were determined from Mg and Mn emission lines in reconstructed spectra employing the Saha-Boltzmann plot method. The SP plasma revealed strongly inhomogeneous emissivity and plasma temperature. Re-excitation of plasma by a second laser pulse (DP) and by an arc discharge (SP-AD) homogenized the plasma and reduced the spatial variation of ε and Te. The homogenization of a plasma is a promising approach to increase the accuracy of calibration-free LIBS analysis of complex materials.
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.