Tuning the Properties of CZTS Films by Controlling the Process Parameters in Cost-Effective Non-vacuum Technique

Highly dispersive Cu₂ZnSnS₄ (CZTS) nanoparticles were successfully synthesized by a simple solvothermal route. A low cost, non-vacuum method was used to deposit CZTS nanoparticle ink on glass substrates by a doctor blade process followed by selenization in a tube furnace to form Cu₂ZnSn (S,Se)₄ (CZTSSe) layers. Different selenization conditions and particle concentrations were considered in order to improve the crystallinity and surface morphology; the annealing temperature was varied between 400°C and 550°C and the annealing time was varied between 5 min and 20 min in a selenium-nitrogen atmosphere. The influence of annealing conditions on structural, compositional, optical and electrical properties of CZTSSe thin films was studied. An improvement in the structural and surface morphology was observed with increasing of annealing temperature (up to 500°C). An enhancement in the crystallinity and surface morphology were observed for thin films annealed for 10–15 min. Absorption study revealed that the band gap energy of as-deposited CZTS thin film was approximately 1.43 eV, while for CZTSSe thin films it ranged from 1.15 eV to 1.34 eV at different annealing temperatures, and from 1.33 eV to 1.38 eV for different annealing times.     

Analysis of the diffusive mass transport in the anode side porous backing layer of a direct methanol fuel cell

In this work we present an analysis of the mass transport in the anode side porous backing layer of a direct methanol fuel cell (DMFC). The effective transport coefficient of different backing layers at various compressions was measured and compared to two different literature models and a single particle random walk simulation which accounts for details of the geometrical fibre microstructure. Based on the measured values of the effective transport coefficient limiting current densities for diffusive transport were calculated taking into account geometric boundary conditions and anisotropic and inhomogeneous backing layer properties. Comparison with the measured values for the limiting current in fuel cell operation shows qualitative agreement. A systematic underestimation indicates that also other transport processes contribute significantly to the mass transfer at the used experimental setup.     

Process engineering parameters and type of n-octenylsuccinate-derivatised starch affect oxidative stability of microencapsulated long chain polyunsaturated fatty acids

Aim of the present study was to investigate the impact of the emulsifying carrier matrix constituent, n-octenylsuccinate-derivatised (OSA)-starch, and process conditions on physical characteristics and oxidative stability of microencapsulated fish oil. Furthermore, the effect of the drying medium, i.e. air or nitrogen, on lipid oxidation during spray-drying and subsequent storage was investigated. Particle characteristics and lipid oxidation of microencapsulated fish oil were both influenced by the type of OSA-starch and the drying conditions. The highest oxidative stability was observed for fish oil microencapsulated in OSA-starch with the lowest average molecular weight and glucose syrup spray-dried at a moderate temperature setting. Particle characteristics of the microcapsules were not attributable for differences in lipid oxidation during storage. In spray-dried carrier matrix particles, the particle size increased with increasing average molecular weight of the OSA-starch and was attributed to an increase in air inclusion. Thus differences in lipid oxidation of the microencapsulated fish oil were attributed to differences in air inclusion as affected by the type of OSA-starch. In terms of spray-drying under inert conditions and in the presence of air, lipid oxidation of microencapsulated fish oil was rather attributed to oxygen availability in the feed emulsion than in the drying gas.     

Reconsidering the quantitative analysis of organic carbon concentrations in size exclusion chromatography

The evaluation of the molecular size distribution of natural organic matter (NOM) in aquatic environments via size exclusion chromatography (SEC) is important for the understanding of environmental processes such as nutrient cycling and pollutant transport as well as of technical water treatment processes. The use of organic carbon (OC) detectors has become popular in recent studies due to improved availability and quantification possibilities, which supposedly are superior to those of ultraviolet (UV) detectors. A set of 12 NOM samples was used to demonstrate the limitations of online OC detection (OCD) when analyzing complex aquatic organic matter. A novel evaluation approach for SEC data is introduced by combining the information from UV absorbance (UVA) and OCD chromatograms as well as offline total OC (t-OC) and dissolved OC-specific UVA (SUVA) measurements. It could be shown that about 70% of certain OC components were not detected with the OCD system used in this study. For the investigated samples, these types of carbon accounted for up to 72% of the t-OC, i.e. for such NOM samples quantification by OCD is not possible or at least highly questionable. The addition of an oxidant improved the overall oxidation efficiency only slightly. Most likely NOM that predominantly consists of polysaccharides and features a nominal molecular weight of 150 kg/mol or more was responsible for low OCD yields. For future applications, a further improvement of the OCD system would be worthwhile so that quantitative analytical data on the molecular size distribution of NOM and its structural characteristics such as the SUVA distribution can be obtained.     

OpenSocial

Zusammenfassung  OpenSocial definiert eine Menge von Programmierschnittstellen für Anwendungen von Drittanbietern im Kontext sozialer Netzwerke. Unter der Verwendung von Standardtechnologien wie HTML und JavaScript k?nnen Entwickler interoperable Anwendungen erstellen, die auf den sozialen Graphen des jeweiligen Netzwerks zugreifen.     

Climate policy impacts on the competitiveness of energy-intensive manufacturing sectors

This study examines the impacts of energy price changes resulting from different carbon-pricing policies on the competitiveness of selected US energy-intensive industries. It further examines possible industry responses, and identifies and provides a preliminary evaluation of potential opportunities to mitigate these impacts. The industry sectors investigated – steel, aluminum, chemicals and paper – are among the largest industrial users of fossil fuels in the US economy. The results of this examination show that climate policies that put a price on carbon could have substantial impacts on the competitiveness of US energy-intensive manufacturing sectors over the next two decades, if climate regulations are applied only in the United States, and no action is taken to invest in advanced low- and no-carbon technologies. The extent of these impacts will vary across industries, depending on their energy intensities, the mix of energy sources they rely on and how energy is used in production activities (heat and power, feedstock). Of relevance is also the speed and rigor with which industries adopt new technologies and retire (or replace) old ones. Other factors affecting these impacts include an industry's vulnerability to foreign imports and its ability to pass through cost increases to its customers in the face of international market competition.     

Supervised data analysis and reliability estimation with exemplary application for spectral data

The analysis and classification of data is a common task in multiple fields of experimental research such as bioinformatics, medicine, satellite remote sensing or chemometrics leading to new challenges for an appropriate analysis. For this purpose different machine learning methods have been proposed. These methods usually do not provide information about the reliability of the classification. This, however, is a common requirement in, e.g. medicine and biology. In this line the present contribution offers an approach to enhance classifiers with reliability estimates in the context of prototype vector quantization. This extension can also be used to optimize precision or recall of the classifier system and to determine items which are not classifiable. This can lead to significantly improved classification results. The method is exemplarily presented on satellite remote spectral data but is applicable to a wider range of data sets.     

Yielding of the clamped-wire system in the Ilizarov external fixator

This study demonstrates that the clamped-wire system used to suspend bones within an Ilizarov external fixator yields when the perpendicular load exceeds 50 N per wire. Cyclic loading was applied to tensioned wires clamped within an Ilizarov ring component, with steadily increasing load amplitude. Wires were tested at four initial tension settings. The amount of energy lost within the clamped-wire system per load cycle was calculated for every test. The results showed that there was a consistent trend to increasing non-recoverable energy loss per load cycle when peak loads exceed 50 N for all initial tension settings. A finite element (FE) model replicating the experimental conditions was performed to investigate the levels of stress within the loaded wires. The FE model analyses showed that high stresses were generated in the wires close to the clamping sites, and that the stress levels could reasonably be expected to exceed the material yield stress when loaded to about 55 N, for all initial tension settings. The results show that material yield, accompanied by some wire slippage through the clamps, is responsible for system yield, in agreement with previous studies. Although the initial wire tension has an appreciable effect on the wire stiffness, it did not affect the elastic load range of the clamped-wire system. To prevent yield of the clamped-wire system in practice, the fixator should be assembled with sufficient wires to ensure that the load transmitted to each wire by the patient does not exceed 50 N.     

A first step towards a simple in-line shape compensation routine for the roll forming of high strength steel

The roll forming process is increasingly used in the automotive industry for the manufacture of structural and crash components from Ultra High Strength Steel (UHSS). Due to the high strength of UHSS (<1GPa) even small and commonly observed material property variations from coil to coil can result in significant changes in material yield and through that affect the final shape of the roll formed component. This requires the re-adjustment of tooling to compensate for shape defects and maintain part geometry resulting in costly downtimes of equipment. This paper presents a first step towards an in-line shape compensation method that based on the monitoring of roll load and torque allows for the estimation of shape defects and the subsequent re-adjustment of tooling for compensation. For this the effect of material property variation on common shape defects observed in the roll forming process as well as measurable process parameters such as roll load and torque needs to be understood. The effect of yield strength and material hardening on roll load and torque as well as longitudinal bow is investigated via experimental trials and numerical analysis. A regression analysis combined with Analysis of Variance (ANOVA) techniques is employed to establish the relationships between the process and material parameters and to determine their percentage influence on longitudinal bow, roll load and torque. The study will show that the level of longitudinal bow, one of the major shape defects observed in roll forming, can be estimated by variations in roll load and torque.