TY - JOUR A1 - Bandi, V.G. A1 - Luciano, M.P. A1 - Saccomano, M. A1 - Patel, N.L. A1 - Bischof, Th. S. A1 - Lingg, J.G.P. A1 - Tsrunchev, P.T. A1 - Nix, M.N. A1 - Ruehle, Bastian A1 - Sanders, C. A1 - Riffle, L. A1 - Robinson, C.M. A1 - Difilippantonio, S. A1 - Kalen, J.D. A1 - Resch-Genger, Ute A1 - Ivanic, J. A1 - Bruns, O.T. A1 - Schnermann, M. T1 - Targeted multicolor in vivo imaging over 1,000 nm enabled by nonamethine cyanines N2 - Recent progress has shown that using wavelengths between 1,000 and 2,000 nm, referred to as the shortwave-infrared or near-infrared (NIR)-II range, can enable high-resolution in vivo imaging at depths not possible with conventional optical wavelengths. However, few bioconjugatable probes of the type that have proven invaluable for multiplexed imaging in the visible and NIR range are available for imaging these wavelengths. Using rational design, we have generated persulfonated indocyanine dyes with absorbance maxima at 872 and 1,072 nm through catechol-ring and aryl-ring fusion, respectively, onto the nonamethine scaffold. Multiplexed two-color and three-color in vivo imaging using monoclonal antibody and dextran conjugates in several tumor models illustrate the benefits of concurrent labeling of the tumor and healthy surrounding tissue and lymphatics. These efforts are enabled by complementary advances in a custom-built NIR/shortwave-infrared imaging setup and software package for multicolor real-time imaging. KW - Photoluminescence KW - Fluorescence KW - Dye KW - Cyanine KW - Antibody KW - Bioconjugate KW - Conjugate KW - Quantum yield KW - Photophysics KW - Lifetime KW - Sensor KW - NIR KW - SWIR KW - Mechanism KW - Imaging KW - Application KW - Contrast agent KW - Bioimaging PY - 2021 DO - https://doi.org/10.1038/s41592-022-01394-6 VL - 19 IS - 3 SP - 353 EP - 358 PB - Nature Research AN - OPUS4-54465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - The potential of Material Acceleration Platforms (MAPs) for creating resilient and sustainable technology value chains N2 - Material Acceleration Platforms (MAPs) represent a transformative approach to the development of resilient and sustainable technology value chains. These platforms can identify candidate chemistries and structures via simulations, and database searches and leverage machine learning-based rapid screening to accelerate the discovery and deployment of novel materials, thereby addressing critical challenges in modern technology sectors. Incorporating high-fidelity advanced characterization in the early phases of material development is crucial for early de-risking. Advanced characterization techniques, such as X-ray diffraction, advanced electrochemical and spectroscopic techniques provide comprehensive insights into the structural, chemical, and physical properties of materials. Long-term testing further contributes to the de-risking process by evaluating the durability and stability of materials under various environmental and operational conditions. Early identification of potential degradation mechanisms enables the refinement of material compositions and processing methods, ultimately leading to the development of more resilient materials. Early upscaling attempts are integral to assessing the feasibility of material leads generated through machine learning-based rapid screening to evaluate the scalability of synthesis and processing techniques. This step is critical for identifying potential challenges in manufacturing, such as issues related to reproducibility, yield, and cost-effectiveness. Process design has to be a major part of the MAP-based material design to cope with the increasing share of secondary raw materials in supply chains. This presentation will briefly summarize possible strategies to address these issues and provide deep-dives on best practices. As the demand for advanced materials continues to grow, MAPs will play an increasingly vital role in driving technological advancements and addressing global challenges. T2 - Materials Week Cyprus 2024 CY - Limassol, Cyprus DA - 17.06.2024 KW - MAPs KW - SDLs KW - Sustainability PY - 2024 AN - OPUS4-60379 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano and Advanced Materials Synthesis N2 - Nano- and advanced materials have been recognized as a key enabling technology of the 21st century, due to their high potential of driving innovations in new clean energy technologies, sustainable manufacturing by substitution of critical raw materials and replacement of hazardous substances, breakthroughs in energy conversion and storage, improvement of the environmental performance of products and processes, and facilitation of circularity. Consequently, new tools that enhance the development and optimization cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future. T2 - MRS Fall Meeting 2025 CY - Boston, MA, USA DA - 30.11.2025 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation PY - 2025 AN - OPUS4-65129 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zaki, Mohammad A1 - Prinz, Carsten A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano- and Advanced Materials Synthesis N2 - The recent emergence of self-driving laboratories (SDL) and material acceleration platforms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting contributions. Here, we describe in detail MINERVA, an SDL specifically built and designed for the synthesis, purification, and in line characterization of nano- and advanced materials. By fully automating these three process steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. We further study the reproducibility and particle size distributions of these seven representative materials in depth and show the excellent performance of the platform when synthesizing these material classes. Lastly, we discuss the design considerations as well as the hardware and software components that went into building the platform and make all of the components publicly available. KW - Self-driving laboratories KW - Materials acceleration platforms KW - Nanomaterials KW - Advanced materials KW - Automation KW - Robotics KW - In-line characterization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627361 DO - https://doi.org/10.1021/acsnano.4c17504 SN - 1936-086X VL - 19 IS - 9 SP - 9029 EP - 9041 PB - ACS Publications CY - Washington, DC AN - OPUS4-62736 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - MINERVA-OS: The Orchestrator of our SDL for Nano and Advanced Materials Synthesis N2 - We present our SDL "Minerva" and its' Orchestrator "Minerva-OS". We discuss the general architecture of the orchestrator, the problems our orchestration solution solves, associated objectives, and success criteria. We demonstrate how a user would interact with it and give examples of what it has already been used for. We also provide some explanation of the available and planned features, and how workflows/experiments are represented. Lastly, we discuss key technical challenges we faced during development. T2 - Orchestration Alignment Virtual Workshop CY - Online meeting DA - 20.01.2026 KW - Self Driving Labs KW - Materials Acceleration Platforms KW - Workflows KW - Orchestration PY - 2026 AN - OPUS4-65422 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano and Advanced Materials Synthesis in a Self-Driving Lab N2 - In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials, that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. T2 - Series on Digitalisation-Meet the Experts | Special Topic: Automation CY - Berlin, Germany DA - 27.02.2026 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation KW - Digitalization PY - 2026 AN - OPUS4-65604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - The Role of Analytics for Closing the Loop in Self-Driving Labs N2 - The recent emergence of self-driving laboratories (SDL) and material acceleration plat-forms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting con-tributions. Consequently, new tools that enhance the development and optimization cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nano-materials – with automated characterization and data analysis, for a complete and reli-able nanomaterial synthesis workflow. By automating the processing and characteriza-tion steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility, reproducibility, and flexibility of the platform. The system also incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). In general, the interface with data analysis algorithms from in-line, at-line, and off-line measure-ments is of great importance for closing the design-make-test-analyze cycle and using these platforms efficiently. Here, we will give examples of how automatic image seg-mentation of electron microscopy images with the help of AI [2] can be used for reduc-ing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a com-mon description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future. T2 - Analytica Conference 2026 CY - Munich, Germany DA - 24.03.2026 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation KW - Digitalization KW - Ontologies PY - 2026 AN - OPUS4-65760 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -