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    GRK 3144: Multiscale Imaging and Analytics of Interfaces in Musculoskeletal Health
    The interdisciplinary Research Training Group (RTG) program entitled "Multiscale Imaging and Analytics of Interfaces in Musculoskeletal Health" extends beyond traditional assessments of bone quantity and density to investigate the complexities of musculoskeletal integrity. This program seeks to understand the interactions between bones, muscles, tendons, fat, and vessels, including their interfaces. By integrating biomedical and technological insights, the program aims to drive innovation in diagnostics and preventive healthcare. Its research objectives include exploring the muscle-fat-bone axis, the tendon-bone interface, the role of vascularization in skeletal health, advanced imaging techniques, models for tissue interfaces, and the use of posture and motion as health indicators. Graduates will gain proficiency in state-of-the-art imaging, disease modeling, and machine learning techniques focused on interfaces, equipping them for addressing global challenges in medicine and technology. The Qualification Program is designed to train a new generation of interdisciplinary STEM scientists in musculoskeletal research. Spanning three cohorts over nine years, it emphasizes skill development in interdisciplinary project management, research ethics, and foundational musculoskeletal research knowledge. Networking opportunities are facilitated through workshops, industry excursions, and international conferences. The inclusion of a Mercator fellow position and the involvement of physicians promote international collaboration and research profile development. This program prepares students for careers in research, teaching, and industry, with a strong emphasis on ethical conduct, collaborative skills, and independence. Located in Hamburg, the RTG benefits from the Interdisciplinary Competence Center for Interface Research (ICCIR) and operates across five laboratory campuses in Hamburg-Eppendorf, Hamburg-Harburg, and Hamburg’s Science City Bahrenfeld. The University Medical Center Hamburg (UKE) provides an interdisciplinary research environment, laboratory facilities, and high-resolution imaging tools. The Hamburg University of Technology (TUHH) specializes in engineering sciences, with a focus on artificial intelligence, data analysis, and computational modeling. Hereon, a member of the Helmholtz Association, offers large-scale facilities for synchrotron science and bone interface research. Hamburg's strategic positioning, robust research infrastructure, and collaborative culture create an optimal setting for musculoskeletal research. The RTG is committed to promoting diversity and gender equality, providing support and initiatives for female scientists. Our interdisciplinary approach not only strengthens healthcare and technological innovation but also prepares graduates for significant roles in academic and corporate research sectors, benefiting both the local and global research communities.
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    EXC 3120 BlueMat - Cross Area Data
    The CA Data is dedicated to the comprehensive management of research data, facilitating seamless interactions across and within the various RAs of BlueMat and beyond (see Sect. 5.3 on Research Data Management (RDM)). CA Data aims to establish a robust infrastructure, provide essential tools, and host RDM experts to ensure a sustainable data lifecycle, encompassing data from both experiments and simulations.
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    EXC 3120 BlueMat - Research Topic A1: Hydrogen-Driven Nanoporous Materials
    We will develop a hydrogen-enabled acoustic metamaterial. The key parameter is the wavelength of sound, which needs to be (1) small (in the order of mm to cm) to enable miniaturization and (2) tunable to enable device switching. The wavelength is determined by the ratio of elastic modulus to mass density. As these parameters are usually strongly correlated, mate- rials with the required properties do not currently exist. A promising approach, demonstrated in preliminary research by the members of the RA A team, is np-Pd, which substantially changes its elastic compliance when alloyed with hydrogen. Thus, a metal/water hybrid nanomaterial achieves switching when hydrogen is relocated between its metal and water phases under the control of an electric potential bias. This new effect implies that one and the same metallic body can be reversibly switched between a hard-matter-like elastic response characteristic of metals and a supercompliant characteristic of soft matter, such as polymers or biological tissue. The key scientific questions are: How can the underlying materials design be shaped in terms of base metal composition and microstructure? How can the open-system elastic response in a nanoscale material with complex microstructural geometry be understood? How can processing routes for components that meet the requirements of Topic A4 be established?
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    EXC 3120 BlueMat - Research Topic A4: Bioinspired and Modeling-Guided Nanoporous Device Concepts
    3D hybrid nanomaterial systems developed in A1 to A3 exhibit a variety of water-driven functionalities, including electrochemo-mechanical coupling that enables an active control of acoustic properties (A1, A2), sensing and actuation (A2, A3), and transfer of large actuation energies in metal/polymer/water hybrid nanomaterials (A2, A3) that can be tuned via an external chemical or physical stimulus. By integrating these systems in A4, we aim to achieve applications with high flexibility and even free programmability. Inspiration from biological principles and advanced modeling approaches serve as sources for creativity and innovation, providing a theoretical proof of concept. Key scientific questions include: How can we translate concepts from biology, such as autonomous sensing and versatile shape change, into technological systems? How can we independently control defined material volumes within a material system via an external stimulus? How can we predict the macroscopic behavior of such multiscale systems to identify the most promising designs?
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    EXC 3120 BlueMat - Cross Area Modeling
    The characteristic behavior of water-driven materials results to a large extent from complex and as yet often poorly understood interactions across different scales. To unravel the fundamental physics and chemistry of water-driven materials, (1) we will develop novel multiscale modeling methods bridging from the quantum to the device level; (2) machine learning (ML) will help to bridge between the scales and leverage the unique imaging data collected in the CA Imaging. Including physical knowledge into ML architectures will help accelerate the design of water-driven materials by identifying, for example, process-structure-property relationships in a fast and automated manner (physics-informed ML). To achieve these ambitious goals, we will rely on extensive (3) research software engineering, providing a whole ecosystem of cutting-edge open-source research software. Overall, we will create a unique multiscale and multi-purpose open-source software ecosystem for computer simulation and ML to understand, predict and optimize Blue Materials from the quantum level to the device level, laying the foundations to leverage cutting-edge imaging and AI to go from “nature-inspired materials” to materials and functionalities “beyond nature”
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    Emmy Noether: Computational Soft Material Mechanics Intelligence
    Polymer materials are essential constituents not only of advanced engineered systems but also of everyday products like food, clothes, drugs and consumables, and also all biological organisms consist to a large extent of soft materials. Modeling the mechanical behavior of such materials has proven particularly challenging because they often exhibit complex types of nonlinearity, anisotropy and inelasticity. Descriptive modeling of soft materials therefore still often requires a large amount of expert knowledge. An even greater challenge are predictions how the behavior of soft materials can be tuned by subtle changes of their microstructure and processing. So far such predictions are possible only to a very limited extend and again with considerable expert knowledge. This project aims at leveraging most recent advances in physics-informed machine learning to automate both the descriptive and predictive modeling of soft materials to an unprecedented degree. Thereby, it will create a powerful basis for the future virtual design of soft materials. Moreover, by automating the pipeline from probing to describing to predicting the mechanical behavior of soft materials, the methods developed in in this project will enable robots to examine and handle also unknown and complex soft materials with an unprecedented degree of autonomy. This can be transformative for biomedical and biomechanical robotics, where, for example, surgical robots are facing in each patient or individual a different behavior of soft tissue and have to learn to probe and handle it. To showcase such benefits for medical robotics, this project will not only develop a toolbox of machine-learning based methods for describing and predicting the mechanical behavior of soft materials in a largely automated way but it will also illustrate their application in a simple robot demonstrator in the laboratory.
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    Identification of optimal corrosion inhibitors for bare and PEO-coated magnesium alloy by combining machine learning and robotic testing
    The major goal of the proposed project is a shift of paradigm for the development of active anti-corrosion coatings using the example of the specific magnesium alloy AZ31. We will demonstrate how machine learning (ML) combined with robotic testing can assist the identification of optimal corrosion inhibitors for bare and PEO-treated magnesium. Highly automated robotic testing will be used to create a large basis of experimental data characterizing the corrosion inhibition efficiency (IE) of a large number of different chemical compounds for AZ31. In parallel, we will develop a machine learning architecture that can benefit from the collected data to predict the corrosion IE of chemical compounds, paving the way towards computer-aided identification of promising corrosion inhibiting compounds. In doing to, our ML architecture will use also data from density functional theory (DFT) calculations, which help to reduce the amount of experimental data required for meaningful predictions. Particularly promising magnesium corrosion inhibitors identified in the course of this project will be examined in depth. Their corrosion inhibition mechanisms will be uncovered by employing a combination of electrochemical techniques accompanied by DFT calculations. The approach pursued in this project will pioneer a new paradigm for the automated and accelerated enhancement of corrosion protection, allowing the accurate and fast identification of the most suitable corrosion inhibitors for a given material from a large experimental database of potentially available corrosion inhibiting compounds.
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