TY - CONF A1 - Zaki, Mohammad T1 - Nano-And Advanced Materials Synthesis In A Self-Driving Lab (SDL) N2 - Nano- and advanced materials are recognized as key enabling technologies of the 21st century, offering exceptional potential to drive innovation and tackle pressing challenges in materials science. To fully realize this potential, it is essential to develop and improve tools that accelerate their design, development, and optimization. Recognizing this pressing need, we present a Self-Driving Lab (SDL) 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 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. The system also exhibits modularity, impressive adaptability in terms of reactions scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses. Automated characterization and data analysis is complemented by a machine learning–driven feedback loop employing active learning algorithms (e.g., Bayesian optimization, artificial neural networks, and downhill simplex methods) to iteratively suggest new experimental parameters toward desired material properties. Therefore, the excellent reproducibility for material syntheses when run on the SDL platform multiple times, the material agnostic behavior, the adaptability, and modularity, underscore the SDL’s reliability and potential as a transformative tool for advancing the development and applications of nano- and advanced materials, offering solutions for a sustainable future. T2 - Gordon Research Seminar and Conference, Multifunctional Materials and Structures-conference CY - Ventura, CA, USA DA - 24.01.2026 KW - Self-Driving Laboratories KW - Materials Acceleration Platforms KW - Nanomaterials PY - 2026 AN - OPUS4-65597 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zaki, Mohammad T1 - Nano- and Advanced Materials Synthesis in a Self-Driving Lab N2 - Nano- and advanced materials are recognized as key enabling technologies of the 21st century, offering exceptional potential to drive innovation and tackle pressing challenges in materials science [1]. To fully realize this potential, it is essential to develop and improve tools that accelerate their design, development, and optimization. Recognizing this pressing need, we present a Self-Driving Lab (SDL) [2] 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 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. The system also exhibits modularity, impressive adaptability in terms of reactions scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses [3]. Automated characterization and data analysis is complemented by a machine learning–driven feedback loop employing active learning algorithms (e.g., Bayesian optimization, artificial neural networks, and downhill simplex methods) to iteratively suggest new experimental parameters toward desired material properties. Therefore, the excellent reproducibility for material syntheses when run on the SDL platform multiple times, the material agnostic behavior, the adaptability, and modularity, underscore the SDL’s reliability and potential as a transformative tool for advancing the development and applications of nano- and advanced materials, offering solutions for a sustainable future. T2 - Gordon Research Seminar and Conference CY - Ventura, CA, USA DA - 24.01.2026 KW - Self-Driving Laboratories KW - Materials Acceleration Platforms KW - Nanomaterials PY - 2026 AN - OPUS4-65596 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 - JOUR A1 - Ruehle, Bastian T1 - Natural language processing for automated workflow and knowledge graph generation in self-driving labs N2 - Natural language processing with the help of large language models such as ChatGPT has become ubiquitous in many software applications and allows users to interact even with complex hardware or software in an intuitive way. The recent concepts of Self-Driving Labs and Material Acceleration Platforms stand to benefit greatly from making them more accessible to a broader scientific community through enhanced user-friendliness or even completely automated ways of generating experimental workflows that can be run on the complex hardware of the platform from user input or previously published procedures. Here, two new datasets with over 1.5 million experimental procedures and their (semi)automatic annotations as action graphs, i.e., structured output, were created and used for training two different transformer-based large language models. These models strike a balance between performance, generality, and fitness for purpose and can be hosted and run on standard consumer-grade hardware. Furthermore, the generation of node graphs from these action graphs as a user-friendly and intuitive way of visualizing and modifying synthesis workflows that can be run on the hardware of a Self-Driving Lab or Material Acceleration Platform is explored. Lastly, it is discussed how knowledge graphs – following an ontology imposed by the underlying node setup and software architecture – can be generated from the node graphs. All resources, including the datasets, the fully trained large language models, the node editor, and scripts for querying and visualizing the knowledge graphs are made publicly available. KW - Natural Language Processing KW - Large Language Models KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Workflows KW - Nanomaterials KW - Advanced Materials PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631947 UR - https://github.com/BAMresearch/MAPz_at_BAM/tree/main/Minerva-Workflow-Generator DO - https://doi.org/10.1039/d5dd00063g SN - 2635-098X SP - 1 EP - 10 PB - Royal Society of Chemistry (RSC) AN - OPUS4-63194 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zaki, Mohammad T1 - Nano- And Advanced Materials Synthesis In A Self-driving Lab (SDL) N2 - Development of new nano- and advanced materials - or improvement of existing ones - are important drivers in materials research due to the high importance of these material classes for various applications. Traditional laboratory methods for material development often suffer from reproducibility issues, inefficiencies, human errors, and long experimental optimization times. To overcome these challenges and thus accelerate and optimize the process of material synthesis and discovery, we are building a Self-Driving Lab (SDL), in which we integrate robotics for autonomous nanomaterial synthesis, and automated characterization and data analysis for a complete and reliable nanomaterial synthesis workflow. We also leverage artificial intelligence (AI) and machine learning (ML) algorithms to analyze characterization results and plan new experiments to optimize material properties in an ML-guided active learning feedback loop. Our SDL is very agnostic towards the types of nano- and advanced materials it can synthesize. On the same SDL platform, we successfully synthesized Stober silica, mesoporous silica, copper-oxide, and gold nanoparticles, as well as metal-organic frameworks and more complex structures from multi-step reactions, such as Au@SiO2 and CuO@SiO2 core-shell nanoparticles. All these material syntheses showed excellent reproducibility when run on the SDL platform multiple times. Automated, in-line characterization measurements of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials have also been incorporated in the SDL, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses [1]. Incorporating these characterization results alongside a machine learning feedback loop that suggests new experimental parameters for obtaining materials with target properties is a key step for developing autonomous, closed-loop optimization processes. In such a process, we typically start by using random sampling to suggest initial experimental parameters, followed by ML-guided active learning algorithms such as Bayesian optimization, artificial neural networks, or downhill simplex optimizers (e.g., Nelder-Mead) that suggest new synthesis parameters to finally arrive at a material with the targeted or enhanced properties. Further improvement and optimization of our SDL has the potential to mitigate challenges faced by traditional approaches and open a way for rapid and reproducible nano- and advanced material synthesis, optimization, and discovery. T2 - ANAKON Conference 2025 CY - Leipzig, Germany DA - 10.03.2025 KW - Self-driving laboratories KW - Materials acceleration platforms KW - Nanomaterials KW - Advanced materials KW - Automation KW - Robotics KW - In-line characterization PY - 2025 AN - OPUS4-62737 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - Workflow generation, management, and semantic description for Self-Driving Labs N2 - The software backend that controls the robotic hardware and runs the synthesis workflows is a very important component of any Self-Driving Lab (SDL). On the one hand, it has to deal with orchestrating and managing complex and task-specific hardware through low-level communication protocols and plan and use the available resources as efficiently as possible while executing (parallelized) workflows, on the other hand, it is the interface the users use to communicate with this highly complex platform, and as such, it needs to be as helpful and user-friendly as possible. This includes the AI-aided experimental design in which the system helps the user to decide which experiment to run next, providing automated data analysis from characterization measurements, and offering easy to understand tools and graphical user interfaces for generating the workflows that are executed on the platform. Lastly, the specificity of the workflows and their dependence on the hardware and software of the SDLs necessitates a common description or ontology for making them easily interchangeable and interoperable between different platforms and labs. In this contribution, we present several key aspects of “Minerva-OS”, the central backend that orchestrates the syntheses workflows of our SDL for Nano- and Advanced Materials Syntheses [1]. 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, compare, and show benchmarks of 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 an ontology for representing the process steps of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. T2 - Accelerate 2025 CY - Toronto, Canada DA - 11.08.2025 KW - Nanomaterials KW - Advanced Materials KW - Workflows KW - Machine Learning KW - SDL PY - 2025 AN - OPUS4-63936 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - Nano- and Advanced Materials Synthesis in a Self-Driving Lab (SDL) 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, improving tools that enhance the development and optimization cycle of nano- and advanced materials is 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 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. 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) of the nanomaterials. 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. 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 - Accelerate 2025 CY - Toronto, Canada DA - 11.08.2025 KW - Nanomaterials KW - Advanced Materials KW - Automation KW - SDL KW - MAP PY - 2025 AN - OPUS4-63935 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - Nano- and Advanced Materials Synthesis in a Self-Driving Lab (SDL) 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, improving tools that enhance the development and optimization cycle of nano- and advanced materials is 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 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. 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) of the nanomaterials. 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. 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 - Accelerate 2025 CY - Toronto, Canada DA - 11.08.2025 KW - Nanomaterials KW - Advanced Materials KW - Automation KW - SDL KW - MAP PY - 2025 AN - OPUS4-63934 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bresch, Harald T1 - Nanoparticle Characterisation - The long way to standardisation N2 - Diese Präsentation gibt einen Überblick über die Entwicklung der Nanopartikelforschung von ca. 2005 bis heute. Beginnend mit den Besonderheiten von Nanopartikeln und der Aufnahme in den menschlichen Körper über Messmethoden bis hin zur Entwicklung einer Prüfrichtlinie im Rahmen der OECD und einem Ausblick über die absehbaren digitalen Entwicklungen. T2 - Abteilungsseminar der Abteilung 4 CY - Berlin, Germany DA - 27.02.2025 KW - Nanomaterials KW - Nano KW - OECD KW - Standardisierung KW - Advanced Materials PY - 2025 AN - OPUS4-64977 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 -