621 Angewandte Physik
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Silicon photonic micro-ring resonators (MRR) developed on the silicon-on-insulator (SOI) platform, owing to their high sensitivity and small footprint, show great potential for many chemical and biological sensing applications such as label-free detection in environmental monitoring, biomedical engineering, and food analysis. In this tutorial, we provide the theoretical background and give design guidelines for SOI-based MRR as well as examples of surface functionalization procedures for label-free detection of molecules.
After introducing the advantages and perspectives of MRR, fundamentals of MRR are described in detail, followed by an introduction to the fabrication methods, which are based on a complementary metal-oxide semiconductor (CMOS) technology. Optimization of MRR for chemical and biological sensing is provided, with special emphasis on the optimization of waveguide geometry. At this point, the difference between chemical bulk sensing and label-free surface sensing is explained, and definitions like waveguide sensitivity, ring sensitivity, overall sensitivity as well as the limit of detection (LoD) of MRR are introduced. Further, we show and explain chemical bulk sensing of sodium chloride (NaCl) in water and provide a recipe for label-free surface sensing.
Summary In the course of classical optics manufacturing glass components are in permanent direct contact with aqueous operating materials. Such contact leads to a certain absorption of water and hydrogenous compounds that may induce severe glass defects. In this contribution, absorption of hydrogen and other contaminants during grinding of glass was observed and qualitatively detected via laser-induced breakdown spectroscopy. It is shown that hydrogen, calcium, magnesium, and carbon are implanted into the glass surface where the contaminant concentration increases over grinding time or contact time of the glass surface with water and the grinding tool, respectively. The contaminants hydrogen, calcium, and magnesium can be attributed to the water used as lubricant. In contrast, carbon most likely originates from wear debris of the used silicon carbide grinding pads. Several possible mechanisms that lead to such surface contamination of glasses during grinding – diffusion, accumulation in micro cracks as well as the formation of hydrated silica – are finally introduced.
Electro-optical properties of doped polymers with high transparency in the visible wavelength range
(2021)
The electro-optical (EO) properties of poly(methyl methacrylate) and the photopolymer poly(vinyl cinnamate) doped with varying concentrations of the EO chromophore 2-Methyl-4-nitroaniline were measured. The EO polymers were embedded in Fabry-Pérot etalons for the simultaneous determination of the Pockels and Kerr coefficients from measurements of the fringe shift induced by an external electric field. It was found that the host polymer has a significant impact on the EO performance and that the undoped host polymers exhibit a significant Pockels effect. Moreover, the Kerr effect provides a substantial contribution of 27% to the total change of the refractive index at relatively high electric field strengths of E = 91.2 MV m−1.
Polycrystalline infrared (PIR) fibers are used for numerous applications, one of those being power delivery for CO2 lasers. However, the fiber tip surface's transmittance cannot be increased with conventional antireflection coatings due to the surface unevenness. Antireflection microstructures (ARMs) offer an alternative way of increasing transmittance. In this work, ARMs were fabricated on the fiber tip surface of an AgClBr fiber by single-pulse femtosecond laser ablation. A single-surface transmittance of 92.8% at 10.6 μm, a CO2 laser operation wavelength, was achieved. The proposed method can help significantly improve the systems' efficiency, where power delivery for CO2 lasers or sources operating in the wide wavelength range is required.
Small and medium-sized companies increasingly turning their attention towards the fourth industrial revolution. In order to increase their own long-term competitiveness, there is a growing desire to make production smarter, more efficient, safer and more sustainable through new technologies. Often, however, existing plants cannot be easily replaced by modern equipment. The reasons for this can be high investment costs, excessive downtimes or the unavailability of an equivalent machine. An alternative solution to the purchase of new equipment is the modernisation or expansion of existing systems, also called retrofitting. Thus, this paper deals with the retrofit process of a machine tool, whereby the software architecture of the control unit is the primary concern of this work.
During the last decade production innovation was mainly focused on connectivity aspects. The vision of smart factories running on software, that uses collected machine data, has become true but foremost for leading industrial companies in highly developed countries. Apart from these, production can also be found in non-industrialized craft professions as well as in less developed countries. As digitalization does not necessarily require an industrial or developed setting the latter could possibly benefit from it as well. Socio-cyber-physical production systems have been used to describe the interdependencies of linked production systems but usually focus on highly developed regions as well as for industrial applications. This paper lines out similarities and differences for each case, introduces the concept of cyber-physical production systems (CPPS) and its extension to socio-CPPS (SCPPS), which emphasizes the role of human workers in the production environment. The relation between industrial, non-industrial production and innovations is examined. Furthermore, the widening of SCCPS concepts for non-industrial production is discussed.
Dermatological in vivo measurements are used for various purposes, e.g. health care, development and testing of skin care products or claim support in marketing. Especially for the last two purposes, in vivo measurements are extensive due to the quantity and repeatability of the measurement series. Furthermore, they are performed manually and therefore represent a nonnegligible time and cost factor. A solution to this is the implementation of collaborative robotics for the measurement execution. Due to various body shapes and surface conditions, common static control procedures are not applicable. To solve this problem, spatial information obtained from a stereoscopic camera can be integrated into the robot control process. However, the designated measurement area has to be detected and the spatial information processed. Therefore the authors propose a concept towards segmenting arm areas through a CNN-based object detector and their further processing to perform robot-based in vivo measurements. The paper gives an overview of the utilization of RGB-D images in 2D object detectors and describes the selection of a suitable model for the application. Furthermore the creation, annotation and augmentation of a custom dataset is presented.
Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.
Chiller systems are used in many different applications in both the industrial and the commercial sector. They are considered major energy consumers and thus contribute a non-negligible factor to environmental pollution as well as to the overall operating cost. In addition, chillers, especially in industrial applications, are often associated with high reliability requirements, as unplanned system downtimes are usually costly. As many studies over the past decades have shown, the presence of faults can lead to significant performance degradation and thus higher energy consumption of these systems. Thus, data-driven fault detection plays an ever-increasing role in terms of energy efficient control strategies. However, labelled data to train associated algorithms are often only available to a limited extent, which consequently inhibits the broad application of such technologies. Therefore, this paper presents an approach that exploits only a small amount of labelled and large amounts of unlabelled data in the training phase in order to detect fault related anomalies. For this, the model utilizes the residual space of the data transformed through principal component analyses in conjunction with a biased support vector machine, which can be ascribed to the concept of semi-supervised learning, or more specifically, positive-unlabelled learning.
To achieve climate goals and contain further global warming, it is inevitable to reduce CO2 emissions especially in energy consumption. A way to do so is by integrating renewable energy sources (RES) into an energy system’s power generation. However, there is no standard procedure to decarbonise a locally restricted system. Therefore, the various local conditions have to be analysed and taken into consideration.
The authors propose a model-based approach to decarbonise the energy system of the island Föhr, Germany. This includes various collected data sets on local conditions such as climate data and heat and power demand. The data is used to represent the island’s energy system and design a model-based solution in a simulation software.
The authors identify potentials by comparing costs and revenues by addressing the deployment of different RES technologies. One finding is that heat generation causes 91 % of CO2 emissions making it the major producer. However, with the designed solution, emissions could be reduced to a third.