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Optimization and validation of analytical affinity chromatography for the in-process monitoring and quantification of peptides containing a C-tag
No full textAntimicrobial peptides and proteins (AMPs) are promising alternatives to conventional antibiotics for the treatment of infections caused by multidrug-resistant bacteria. The production of recombinant AMPs is facilitated by platform technologies such as the C-tag, a sequence of four C-terminal amino acids that allows immunoaffinity capture and purification. However, the detection and quantification of such products throughout the manufacturing process is a significant challenge. We therefore used a design of experiments approach to optimize a novel high-throughput analytical immunoaffinity chromatography method for the accurate quantification of AMPs containing a C-tag, resulting in minimal analyte carryover (98.8 ± 0.1 % product elution). We then validated the method in accordance with International Conference on Harmonisation guideline Q2(R2). Validation confirmed that the method achieves high specificity, linearity, accuracy, and precision. We implemented in-process control and quantification throughout the manufacturing process, from cell lysis to the final purified product. We found that the lysate and acidic samples (pH < 2) can lead to deviations. However, following sample pretreatment, C-tag quantification reduced the error to ≤ 4 %, which is potentially superior to current non-specific quantification methods such as UV absorbance and colorimetry. Implementing this method for in-process control and quantification throughout the manufacturing process achieves the reliable assessment of product quantity and quality. This method also offers improvements over the product-specific enzyme-linked immunosorbent assay currently used for C-tagged products because it has a higher precision, accuracy and throughput, with a measurement time of 2.5 min per sample. Our analytical affinity chromatography method is therefore a valuable tool for the quantification of AMPs as part of a novel platform technology approach for C-tagged products.122
Introduction to the Data-driven Services in Manufacturing Minitrack - Exploring Management, Engineering, and Organizational Transformation
No full text125612572022-Januar
Dedicated diffraction imaging for sub-seafloor object detection
No full textObjects in the sub-seafloor sediments (boulders or UxO) pose a risk for offshore infrastructure. Diffraction imaging is a promising method for the detection of objects within marine sediments. A non-aliasing record of the wave field allows a separation of reflected and diffracted energy and a subsequent localization of the origin of diffracted energy. Detectable object sizes and maximum sediment penetration depend on the source frequency used and may be adjusted to expected object sizes and the geological setting. Such a new survey method holds considerable benefits for e.g. wind park construction, cable route characterization and some specialized UxO detection applications
Fatigue strength verification of bearing type connections with mechanical fasteners - A comparison of Eurocode 3 and effective notch stress concept
No full text581592The fatigue strength verification is a key factor in the design of cyclical loaded steel and lightweight steel structures. Mechanically fastened joints represent hotspots in these structures that require special attention. The fatigue strength verification acc. to the Eurocode 3 is based on the nominal stress concept with defined detail categories for different constructional details. This method is still the preferred verification procedure due to the ease of use. Most of the influencing parameters on fatigue resistance are already covered by the corresponding detail category of a constructional detail. This allows the verification to be carried out without the need for precise knowledge of these parameters. Associated with this is also a conservatism in the design of some of the constructional details, which is demonstrated by new studies on the fatigue resistance of bearing type connections with mechanical fasteners. In these studies, the material strength and the joint configuration (double covered joint, variation of hole and edge distances, number and type of fasteners) were systematically changed to determine their effects on fatigue resistance. A promising alternative to the nominal stress concept is the effective notch stress concept for non-welded constructional details. This concept makes it possible to consider the two main effects of material strength and the notch effect for bearing type connections in the design. Due to the missing preload in bearing type connections they transfer the acting loads by bearing contact. This results in a combination of different stress increasing effects in the components, which requires a more detailed description by numerical studies for verification. By comparing the test-assisted S-N curves (nominal stress concept) with synthetic S-N curves (effective notch stress concept) the deficits of the existing design rules are highlighted. In addition, a new design proposal for the bearing type connection with mechanical fasteners based on the notch stress concept is proposed.7
Untersuchung des Einflusses des Materialmodells auf die strukturdynamische Simulation einer kurzfaserverstärkten Koppelstange
No full textDesign for TRL – Technologically Mature Design Along Development Processes of Applied Research Design for TRL – Technologiereifes Design entlang von Entwicklungsprozessen der angewandten Forschung
No full text464475This paper describes the development of a process for systematically involving designers in research and development work in the context of applied, technology-oriented research and transfer issues. Design is increasingly indispensable in many areas of life and is widely established as a discipline in industrial development processes. Design is also becoming increasingly important for applied research, as it can help to realise the path of technologies into the market. Technology readiness levels (TRL) allow a standardised assessment of the technology and provide information about its maturity and transferability into a market-relevant implementation. Despite this standardised assessment, in practice non-involved persons often judge subjectively – preferably on the basis of external impressions and in comparison with personal experience rather than objective criteria. The study examines this discrepancy, tests design methods in six projects, and discusses findings. A process is proposed that can ensure a balance of technological and design maturity through structured design methodological support
Development of a hybrid microsystem for acquisition of sterilization cycles
No full text190193In this paper we present a hybrid microsystem for acquisition and counting of sterilisation cycles. The device includes a micromechanical counter mechanism and a thermal actuator based on a shape memory alloy (SMA). The device is designed to count 100 sterilization cycles. The basic functionality is investigated on a hotplate using a thermal temperature profile with a peak temperature of 135°C. In this manner, counting of thermal cycles is demonstrated
Silicon Economy: Logistics as the Natural Data Ecosystem
No full text263278The “Silicon Economy” is synonymous with a coming digital infrastructure (digital ecosystem) based on the automated negotiation, disposition, and control of flows of goods, enabling new, digital business models (not only) for logistics. This infrastructure requires and enables the trading of data without losing sovereignty over the data. It is the digital infrastructure and environment for the highly distributed AI algorithms along value networks. In contrast to oligopolistic developments in the B2C sector (amazon.com, AirBnB, Alibaba, Uber, etc.), the Silicon Economy is a federated and decentralized platform ecosystem, the basic components of which are made available to the general public as open source for free use. The Silicon Economy ecosystem is becoming an enabler of supply chain ecosystems in which goods, autonomously controlled by Artificial Intelligence (AI), undergo orchestrated processes according to the situation. This article focuses on the origins and potentials but also on the technological foundations and challenges of the transformation toward a Silicon Economy
Reusable surrogate models for distillation columns
No full textSurrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks. Despite their popularity, most surrogate models are trained for a narrow range of fixed chemical systems and operating conditions, which limits their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that generalizes across a vast design space. The key enabler is a novel ML-fueled modelfluid representation which allows for the generation of datasets of more than 1000000 samples. This allows the surrogate to generalize not only over column specifications but also over the entire chemical space of homogeneous ternary vapor–liquid mixtures. We validate the model's accuracy and demonstrate its practical utility in a case study on entrainer distillation, where it successfully screens and ranks candidate entrainers, significantly reducing the computational effort compared to rigorous optimization.20
Improving a Deep Learning Temperature-Forecasting Model of a 3-Axis Precision Machine with Domain Randomized Thermal Simulation Data
No full text574584With the continuous rise of industry 4.0 applications, artificial intelligence and data driven monitoring systems for machine tools proved themselves as highly capable alternatives to classical analytical approaches. However, their precision is limited to a number of crucial aspects. One of the main aspects revolves around the lack of meaningful data, which leads to imprecise and false model predictions. This issue is closely linked to production processes and machine tools in production engineering, as the available amount of meaningful real data is strongly limited. The usage of simulation models to acquire additional synthetic data is able to fill this lack. This work looks into improving the prediction accuracy of a deep learning model for temperature forecasting of a 3-axis precision machine by combing and comparing real process data with domain randomized simulation data. The used thermal simulation model is based on finite element models of the machine assemblies. Model order reduction techniques were applied to the FE models to reduce the computational effort, increasing the simulation-to-reality gap. The approach is evaluated on unseen real data, demonstrating the underlying potential of the inclusion of synthetic data from simulation models of machine behavior.Part F116