ABSTRACT
This study presents the synthesis and characterization of metallopolymer networks with a triple shape-memory ability. A covalently crosslinked polymer network featuring two different additional ligands in its side chains is synthesized via free radical polymerization (FRP). The subsequent addition of different metal salts leads to the selective formation of complexes with two different association constants (Ka), proven via isothermal titration calorimetry (ITC). Those two supramolecular crosslinks feature different activation temperatures and can act as two individual switching units enabling the fixation and recovery of two temporary shapes. The presented samples were investigated in a detailed fashion via differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and FT-Raman spectroscopy. Furthermore, thermo-mechanical analyses (TMA) revealed excellent dual and triple shape-memory abilities of the presented metallopolymer networks.
ABSTRACT
A new shape-memory polymer is presented, in which both the stable phase as well as the switching unit consist of two different metal complexes. Suitable metal ions, which simultaneously form labile complexes with histidine and stable ones with terpyridine ligands, are identified via isothermal titration calorimetry (ITC) measurements. Different copolymers are synthesized, which contain butyl methacrylate as the main monomer and the metal-binding ligands in the side chains. Zn(TFMS)2 and NiCl2 are utilized for the dual crosslinking, resulting in the formation of metallopolymer networks. The switching temperature can simply be tuned by changing the composition as well as by the choice of the metal ion. Strain fixity rates (about 99%) and very high strain recovery rates (up to 95%) are achieved and the mechanism is revealed using different techniques such as Raman spectroscopy.
ABSTRACT
The Internet of Things (IoT) domain has been one of the fastest growing areas in the computer industry for the last few years. Consequently, IoT applications are becoming the dominant work load for many data centers. This has implications for the designers of data centers, as they need to meet their customers' requirements. Since it is not easy to use real applications for the design and test of data center setups, a tool is required to emulate real applications but is easy to configure, scale and deploy in a data center. This paper will introduce a simple but generic way to model the work load of typical IoT applications, in order to have a realistic and reproducible way to emulate IT loads for data centers. IoT application designers are in the process of harmonizing their approaches on how architectures should look, which building blocks are needed, and how they should interwork. While all IoT subdomains are diverse when it comes to the details, the architectural blueprints are becoming more and more aligned. These blueprints are called reference architectures and incorporate similar patterns for the underlying application primitives. This paper will introduce an approach to decompose IoT applications into such application primitives, and use them to emulate a workload as it would be created by the modeled application. The paper concludes with an example application of the IoT Workload Emulation in the BodenTypeDC experiment, where new cooling approaches for data centers have been tested under realistic work load conditions.
ABSTRACT
BACKGROUND, AIM AND SCOPE: European legislation stipulates that genetically modified organisms (GMO) have to be monitored to identify potential adverse environmental effects. A wealth of different types of monitoring data from various sources including existing environmental monitoring programmes is expected to accumulate. This requires an information system to efficiently structure, process and evaluate the monitoring data. METHODS: A structure for an Information System for Monitoring GMO (ISMO) was developed by a multidisciplinary research team. It is based on the requirement to organise all relevant information in a logical, readily accessible and functional manner. RESULTS: For the ISMO, we present a combination of three interrelated components: Firstly, an ISMO should comprise a knowledge database structured according to information related to the different scale levels of biological organisation relevant to GMO monitoring and scientific hypotheses on cause-effects which should be validated by monitoring data. Secondly, a monitoring database should be part of an ISMO containing GMO-specific monitoring data and meta-data. This monitoring database should be linked with monitoring data from other monitoring programmes which are relevant for GMO-related questions. Thirdly, an ISMO should encompass a database covering administrative and procedural data. Neither national nor international approaches to an ISMO exist yet. CONCLUSIONS: An ISMO as designed in this paper could support competent authorities in both the GMO notification process and in post-market monitoring. This includes evaluating the environmental risks of experimentally releasing GMO and placing them on the market, assessing monitoring plans and evaluating monitoring results. The ISMO should be implemented on both the national and international level, preferably combining different administrative scales. Harmonisation approaches towards GMO monitoring data are at an initial stage, but they are a precondition to coordinated GMO monitoring and to successfully implementing an ISMO. It is recommended to set up a legal basis and to agree on common strategies for the data coordination and harmonisation.
