A multivariate process monitoring strategy and control concept for a small-scale fermenter in a PAT environment

This work describes a multivariate monitoring and control concept for bioprocesses based on historical process data. The concept is demonstrated for a Saccharomyces Cerevisiae (baker’s yeast) fermentation process executed in a small-scale bioreactor, which is equipped with common probes to analyze the broth and off-gases. The data of “in-control” fermentation processes were evaluated by means of a principal component analysis to define confidence limits for subsequent fermentations. A violation of these limits indicated that a process had to be classified as “out-of-control”. Fault diagnosis was provided by the components of the squared prediction error, which can also be used to determine the appropriate counteractions, e.g. via an expert system control strategy as described in this study. The sensitivity of fault diagnosis was demonstrated via various erroneous runs. The duration of bioprocesses can vary distinctly, which complicates the definition of time dependent control limits. Therefore, this study utilizes a three-component partial least squares regression model to quantify the current batch maturity during the process. This maturity is then used to reference current data to the appropriate historical data and the assigned control limits.     

Flexible and Ultrasoft Inorganic 1D Semiconductor and Heterostructure Systems Based on SnIP

Low dimensionality and high flexibility are key demands for flexible electronic semiconductor devices. SnIP, the first atomic‐scale double helical semiconductor combines structural anisotropy and robustness with exceptional electronic properties. The benefit of the double helix, combined with a diverse structure on the nanoscale, ranging from strong covalent bonding to weak van der Waals interactions, and the large structure and property anisotropy offer substantial potential for applications in energy conversion and water splitting. It represents the next logical step in downscaling the inorganic semiconductors from classical 3D systems, via 2D semiconductors like MXenes or transition metal dichalcogenides, to the first downsizeable, polymer‐like atomic‐scale 1D semiconductor SnIP. SnIP shows intriguing mechanical properties featuring a bulk modulus three times lower than any IV, III‐V, or II‐VI semiconductor. In situ bending tests substantiate that pure SnIP fibers can be bent without an effect on their bonding properties. Organic and inorganic hybrids are prepared illustrating that SnIP is a candidate to fabricate flexible 1D composites for energy conversion and water splitting applications. SnIP@C₃N₄ hybrid forms an unusual soft material core–shell topology with graphenic carbon nitride wrapping around SnIP. A 1D van der Waals heterostructure is formed capable of performing effective water splitting.     

Capturing smart service systems: Development of a domain‐specific modelling language

Over the last years, the nature of service has changed owing to conceptual advances and developments in information technology. These developments have given rise to novel types of service and smart service systems (SSS), ie, resource configurations capable of learning, dynamic adaptation, and decision making. Currently, the internet of things (IoT) is turning physical objects into active smart things, bridging the gap between the physical and the digital world. Smart things advance SSS as they observe the physical environment, access local data, immerse into individuals' everyday lives and organizational routines. In line with the emergent nature of both phenomena, the impact of the IoT on SSS yet needs to be explored. Building the basis for explanatory and design‐led research and for the analysis and design of SSS, a means for the conceptual modelling of SSS that accounts for novel IoT‐enabled concepts is in high need. Hence, we designed, demonstrated, and evaluated a domain‐specific modelling language (DSML) for SSS. We evaluated the DSML by using it in the modelling of real‐world scenarios from all functional IoT domains, by submitting it to the scrutiny of industry experts, by discussing it against generic DSML requirements, and by analysing to what extent it meets domain‐specific design objectives compared with competing artefacts. To demonstrate the DSML, we included a complex real‐world scenario centred around the Nest Learning Thermostat.     

Realistic data transfer scheduling with uncertainty

Next Generation Networks (NGNs) will be comprised of different access technologies. We are already seeing the emergence of mobile devices with the capability of connecting to heterogeneous networks with different capabilities and constraints. In addition, many bandwidth intensive applications have rather relaxed real-time constraints allowing for alternative scheduling mechanisms which can take into account user preferences, network characteristics as well as future network resource availability to better exploit network heterogeneity. The current approaches either simply react to changes, or assume that availability predictions are perfect.In this paper, we propose a scheduling scheme based on stochastic modeling to account for prediction errors. The scheme optimizes overall user utility gain considering imperfect predictions taken over realistic time intervals while catering for different applications’ needs. We use 180 days of real user data of many users to demonstrate that it consistently outperforms other non-stochastic and greedy approaches in typical networking environments.     

Expanding the Horizon of Mechanochromic Detection by Luminescent Shear Stress Sensor Supraparticles

Novel sensor particles have been developed that expand the variety of today's mechanochromic systems. The developed supraparticles consist of several different components to enable the sensor function. First, a luminescence‐quenching core material is coated with silica nanoparticles. Second, this structure is surrounded by raspberry‐like nanostructured silica particles, which host luminophore moieties. Upon shear stress, components spatially separated in the original supraparticles, namely quencher and luminophore components, come into contact. This causes an irreversible quenching of the luminescence (sensor turn‐off). Different options to select core, quencher, and luminophore components allow to drive the sensors to different sensing options regarding response time, sensitivity, and operation time. The sensors can be sensitive and rapid in response or generated to monitor the influence of shear stress over longer periods of time. Thus, a rapid, visible, “on‐the‐fly” sensing of shear stress is possible as well as monitoring the impact of prolonged shear stress. The particles are assembled by spray‐drying. This affords flexibility when choosing the type of quencher and luminophore moiety. As such, the sensitivity of this robust, particle‐based shear stress sensor system can be deliberately configured. Furthermore, the supraparticle sensor can be integrated in surfaces to create interactive, communicating materials.     

Perfluoroalkyl-Functionalized Hyperbranched Polyglycerol as Pore Forming Agents and Supramolecular Hosts in Polymer Microspheres

Perfluoroalkyl-functionalized, hyperbranched polyglycerols that produce stable microbubbles are integrated into a microfluidic emulsion to create porous microspheres. In a previously-presented work a dendrimer with a perfluorinated shell was used. By replacing this dendrimer core with a hyperbranched core and evaluating different core sizes and degrees of fluorinated shell functionalization, we optimized the process to a more convenient synthesis and higher porosities. The new hyperbranched polyglycerol porogens produced more pores and can be used to prepare microspheres with porosity up to 12% (v/v). The presented preparation forms pores with a perfluoroalkyl-functionalized surface that enables the resulting microspheres to act as supramolecular host systems. The microspheres can incorporate gases into the pores and actives in the polymer matrix, while the perfluoroalkylated pore surface can be used to immobilize perfluoro-tagged molecules onto the pores by fluorous-fluorous interaction.     

Current Challenges in Catalyst Development for PEM Water Electrolyzers

This work addresses current challenges in catalyst development for proton exchange membrane water electrolyzers (PEM-WEs). To reduce the amount of iridium at the oxygen anode to levels commensurate with large-scale application of PEM-WEs, high-structured catalysts with a low packing density are required. To allow an efficient development of such catalysts, activity and durability screening tests are essential. Rotating disk electrode measurements are suitable to determine catalyst activity, while accelerated stress tests on the MEA level are required to evaluate catalyst stability.