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The hybrid K+/Ca2+ sensor based on laser scanned silicon transducer for multi-component analysis
(2002)
Low emission zones and truck bans, the rising price of diesel and increases in road tolls: all of these factors are putting serious pressure on the transport industry. Commercial vehicle manufacturers and their suppliers are in the process of identifying new solutions to these challenges as part of their efforts to meet the EEV (enhanced environmentally friendly vehicle) limits, which are currently the most robust European exhaust and emissions standards for trucks and buses.
Biofuels potentially interesting also for aviation purposes are predominantly liquid fuels produced from biomass. The most common biofuels today are biodiesel and bioethanol. Since diesel engines are rather rare in aviation this survey is focusing on ethanol admixed to gasoline products.
The Directive 2003/30/EC of the European Parliament and the Council of May 8th 2003 on the promotion of the use of biofuels or other renewable fuels for transport encourage a growing admixture of biogenic fuel components to fossil automotive gasoline. Some aircraft models equipped with spark ignited piston engines are approved for operation with automotive gasoline, frequently called “MOGAS” (motor gasoline). The majority of those approvals is limited to MOGAS compositions that do not contain methanol or ethanol beyond negligible amounts. In the past years (bio-)MTBE or (bio-)ETBE have been widely used as blending component of automotive gasoline whilst the usage of low-molecular alcohols like methanol or ethanol has been avoided due to the handling problems especially with regard to the strong affinity for water. With rising mandatory bio-admixtures the conversion of the basic biogenic ethanol to ETBE, causing a reduction of energetic payoff, becomes more and more unattractive. Therefore the direct ethanol admixture is accordingly favoured.
Due to the national enforcements of the directive 2003/30/EC more oxygenates produced from organic materials like bioethanol have started to appear in automotive gasolines already. The current fuel specification EN 228 already allows up to 3 % volume per volume (v/v) (bio-)methanol or up to 5 % v/v (bio-)ethanol as fuel components. This is also roughly the amount of biogenic components to comply with the legal requirements to avoid monetary penalties for producers and distributors of fuels.
Since automotive fuel is cheaper than the common aviation gasoline (AVGAS), creates less problems with lead deposits in the engine, and in general produces less pollutants it is strongly favoured by pilots. But being designed for a different set of usage scenarios the use of automotive fuel with low molecular alcohols for aircraft operation may have adverse effects in aviation operation. Increasing amounts of ethanol admixtures impose various changes in the gasoline’s chemical and physical properties, some of them rather unexpected and not within the range of flight experiences even of long-term pilots.
The fourth industrial revolution presents a multitude of challenges for industries, one of which being the increased flexibility required of manufacturing lines as a result of increased consumer demand for individualised products. One solution to tackle this challenge is the digital twin, more specifically the standardised model of a digital twin also known as the asset administration shell. The standardisation of an industry wide communications tool is a critical step in enabling inter-company operations. This paper discusses the current state of asset administration shells, the frameworks used to host them and their problems that need to be addressed. To tackle these issues, we propose an event-based server capable of drastically reducing response times between assets and asset administration shells and a multi-agent system used for the orchestration and deployment of the shells in the field.
The Kremer–Grest (KG) polymer model is a standard model for studying generic polymer properties in molecular dynamics simulations. It owes its popularity to its simplicity and computational efficiency, rather than its ability to represent specific polymers species and conditions. Here we show that by tuning the chain stiffness it is possible to adapt the KG model to model melts of real polymers. In particular, we provide mapping relations from KG to SI units for a wide range of commodity polymers. The connection between the experimental and the KG melts is made at the Kuhn scale, i.e., at the crossover from the chemistry-specific small scale to the universal large scale behavior. We expect Kuhn scale-mapped KG models to faithfully represent universal properties dominated by the large scale conformational statistics and dynamics of flexible polymers. In particular, we observe very good agreement between entanglement moduli of our KG models and the experimental moduli of the target polymers.
Welcome to the training: Actionist Respoke interaktiv by Michael Jonoschek und Rüdiger Schlömer
(2002)
Learning from Brachelen
(2002)
Close interrelations between sound and image are not a mere phenomenon of today’s multimedia technology. The idea of the synthesis of different media lies at the core of the concept of the Gesamtkunstwerk in the second half of the 19th century and it can also be traced back to the synaesthesia debate at the beginning of the 20th century [...].
7T MR Safety
(2021)
The subtilase family (S8), a member of the clan SB of serine proteases are ubiquitous in all kingdoms of life and fulfil different physiological functions. Subtilases are divided in several groups and especially subtilisins are of interest as they are used in various industrial sectors. Therefore, we searched for new subtilisin sequences of the family Bacillaceae using a data mining approach. The obtained 1,400 sequences were phylogenetically classified in the context of the subtilase family. This required an updated comprehensive overview of the different groups within this family. To fill this gap, we conducted a phylogenetic survey of the S8 family with characterised holotypes derived from the MEROPS database. The analysis revealed the presence of eight previously uncharacterised groups and 13 subgroups within the S8 family. The sequences that emerged from the data mining with the set filter parameters were mainly assigned to the subtilisin subgroups of true subtilisins, high-alkaline subtilisins, and phylogenetically intermediate subtilisins and represent an excellent source for new subtilisin candidates.
Subtilisins from microbial sources, especially from the Bacillaceae family, are of particular interest for biotechnological applications and serve the currently growing enzyme market as efficient and novel biocatalysts. Biotechnological applications include use in detergents, cosmetics, leather processing, wastewater treatment and pharmaceuticals. To identify a possible candidate for the enzyme market, here we cloned the gene of the subtilisin SPFA from Fictibacillus arsenicus DSM 15822ᵀ (obtained through a data mining-based search) and expressed it in Bacillus subtilis DB104. After production and purification, the protease showed a molecular mass of 27.57 kDa and a pI of 5.8. SPFA displayed hydrolytic activity at a temperature optimum of 80 °C and a very broad pH optimum between 8.5 and 11.5, with high activity up to pH 12.5. SPFA displayed no NaCl dependence but a high NaCl tolerance, with decreasing activity up to concentrations of 5 m NaCl. The stability enhanced with increasing NaCl concentration. Based on its substrate preference for 10 synthetic peptide 4-nitroanilide substrates with three or four amino acids and its phylogenetic classification, SPFA can be assigned to the subgroup of true subtilisins. Moreover, SPFA exhibited high tolerance to 5% (w/v) SDS and 5% H₂O₂ (v/v). The biochemical properties of SPFA, especially its tolerance of remarkably high pH, SDS and H₂O₂, suggest it has potential for biotechnological applications.
Halophilic and halotolerant microorganisms represent a promising source of salt-tolerant enzymes suitable for various biotechnological applications where high salt concentrations would otherwise limit enzymatic activity. Considering the current growing enzyme market and the need for more efficient and new biocatalysts, the present study aimed at the characterization of a high-alkaline subtilisin from Alkalihalobacillus okhensis Kh10-101T. The protease gene was cloned and expressed in Bacillus subtilis DB104. The recombinant protease SPAO with 269 amino acids belongs to the subfamily of high-alkaline subtilisins. The biochemical characteristics of purified SPAO were analyzed in comparison with subtilisin Carlsberg, Savinase, and BPN'. SPAO, a monomer with a molecular mass of 27.1 kDa, was active over a wide range of pH 6.0–12.0 and temperature 20–80 °C, optimally at pH 9.0–9.5 and 55 °C. The protease is highly oxidatively stable to hydrogen peroxide and retained 58% of residual activity when incubated at 10 °C with 5% (v/v) H2O2 for 1 h while stimulated at 1% (v/v) H2O2. Furthermore, SPAO was very stable and active at NaCl concentrations up to 5.0 m. This study demonstrates the potential of SPAO for biotechnological applications in the future.
The aim of the present study was the characterisation of three true subtilisins and one phylogenetically intermediate subtilisin from halotolerant and halophilic microorganisms. Considering the currently growing enzyme market for efficient and novel biocatalysts, data mining is a promising source for novel, as yet uncharacterised enzymes, especially from halophilic or halotolerant Bacillaceae, which offer great potential to meet industrial needs. Both halophilic bacteria Pontibacillus marinus DSM 16465ᵀ and Alkalibacillus haloalkaliphilus DSM 5271ᵀ and both halotolerant bacteria Metabacillus indicus DSM 16189 and Litchfieldia alkalitelluris DSM 16976ᵀ served as a source for the four new subtilisins SPPM, SPAH, SPMI and SPLA. The protease genes were cloned and expressed in Bacillus subtilis DB104. Purification to apparent homogeneity was achieved by ethanol precipitation, desalting and ion-exchange chromatography. Enzyme activity could be observed between pH 5.0–12.0 with an optimum for SPPM, SPMI and SPLA around pH 9.0 and for SPAH at pH 10.0. The optimal temperature for SPMI and SPLA was 70 °C and for SPPM and SPAH 55 °C and 50 °C, respectively. All proteases showed high stability towards 5% (w/v) SDS and were active even at NaCl concentrations of 5 M. The four proteases demonstrate potential for future biotechnological applications.
Selective Laser Melting (SLM) is one of the Additive Manufacturing (AM) technologies applicable for producing complex geometries which are typically expensive or difficult to fabricate using conventional methods. This process has been extensively investigated experimentally for various metals and the fabrication process parameters have been established for different applications; however, fabricating 3D glass objects using SLM technology has remained a challenge so far although it could have many applications. This paper presents a summery on various experimental evaluations of a material database incorporating the build parameters of glass powder using the SLM process for jewelry applications.
Additive manufacturing (AM) works by creating objects layer by layer in a manner similar to a 2D printer with the “printed” layers stacked on top of each other. The layer-wise manufacturing nature of AM enables fabrication of freeform geometries which cannot be fabricated using conventional manufacturing methods as a one part. Depending on how each layer is created and bonded to the adjacent layers, different AM methods have been developed. In this chapter, the basic terms, common materials, and different methods of AM are described, and their potential applications are discussed.
Experimental investigation of selective laser melting of lunar regolith for in-situ applications
(2013)
The cooling process in induction based crucible melting furnaces for Industrial applications is one of the important and challenging factors in production and safety engineering. Accordingly, proper implementation of the cooling system of the furnace using optimum cooling guides and fail-safe features are critical in order to improve the safety of the process. Regarding this, manufacturing of porous material with high electrical isolation for the drainage segments of the cooling channels is examined in this study. Consequently, various geometries with different porosities using glass and ceramic powder are fabricated using Selective Laser Sintering (SLS) process. The manufactured parts are examined in a prototype furnace testing and the feasibility of the SLS manufacturing of parts for this application is discussed.
Although Selective Laser Melting (SLM) process is an innovative manufacturing method, there are challenges such as inferior mechanical properties of fabricated objects. Regarding this, buckling deformation which is caused by thermal stress is one of the undesired mechanical properties which must be alleviated. As buckling deformation is more observable in hard to process materials, silver is selected to be studied theoretically and experimentally for this paper. Different scanning strategies are utilized and a Finite Element Method (FEM) is applied to calculate the temperature gradient in order to determine its effect on the buckling deformation of the objects from experiments.
Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.