TY - JOUR A1 - Tavernaro, Isabella A1 - Rajotte, Isabelle A1 - Thibeault, Marie-Pier A1 - Sander, Philipp C. A1 - Kodra, Oltion A1 - Lopinski, Gregory A1 - Radnik, Jörg A1 - Johnston, Linda J. A1 - Brinkmann, Andreas A1 - Resch-Genger, Ute T1 - Quantifying surface groups on aminated silica nanoparticles of different size, surface chemistry, and porosity with solution NMR, XPS, optical assays, and potentiometric titration N2 - We assessed the quantification of surface amino functional groups (FGs) for a large set of commercial and custom-made aminated silica nanoparticles (SiO2 NPs) with sizes of 20–100 nm, prepared with different sol–gel routes, different amounts of surface amino FGs, and different porosity with four methods providing different, yet connected measurands in a bilateral study of two laboratories, BAM and NRC, with the overall aim to develop standardizable measurements for surface FG quantification. Special emphasis was dedicated to traceable quantitative magnetic resonance spectroscopy (qNMR) performed with dissolved SiO2 NPs. For the cost efficient and automatable screening of the amount of surface amino FGs done in a first step of this study, the optical fluorescamine assay and a potentiometric titration method were utilized by one partner, i.e., BAM, yielding the amount of primary amino FGs accessible for the reaction with a dye precursor and the total amount of (de)protonatable FGs. These measurements, which give estimates of the minimum and maximum number of surface amino FGs, laid the basis for quantifying the amount of amino silane molecules with chemo-selective qNMR with stepwise fine-tuned workflows, involving centrifugation, drying, weighting, dissolution, measurement, and data evaluation steps jointly performed by BAM and NRC. Data comparability and relative standard deviations (RSDs) obtained by both labs were used as quality measures for method optimization and as prerequisites to identify method-inherent limitations to be later considered for standardized measurement protocols. Additionally, the nitrogen (N) to silicon (Si) ratio in the near-surface region of the SiO2 NPs was determined by both labs using X-ray photoelectron spectroscopy (XPS), a well established surface sensitive analytical method increasingly utilized for microparticles and nano-objects which is currently also in the focus of international standardization activities. Overall, our results underline the importance of multi-method characterization studies for quantifying FGs on NMs involving at least two expert laboratories for effectively identifying sources of uncertainty, validating analytical methods, and deriving NM structure–property relationships. KW - Advanced Materials KW - Amino Groups KW - Calibration KW - Characterization KW - Functional groups KW - Method Comparison KW - Nano Particle KW - Validation KW - XPS KW - Optical Assay KW - Quantification KW - Surface Analysis KW - Reference Materials KW - Synthesis KW - Fluorescence PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-649992 DO - https://doi.org/10.1039/d5na00794a VL - 7 IS - 21 SP - 6888 EP - 6900 PB - Royal Society of Chemistry AN - OPUS4-64999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tavernaro, Isabella T1 - A Multimodal Approach to Quantify Surface Functional Groups and Ligands on Amorphous Silica Nanoparticles N2 - Nowadays amorphous silica nanoparticles (SiO2-NP) are one of the most abundant engineered nanomaterials, that are highly stable and can be easily produced on a large scale at low cost. Surface functionalized SiO2-NP are of great interest in the life and material sciences, as they can be used e.g. as drug carriers, fluorescent sensors, and multimodal labels in bioanalytical assays and imaging applications. Their performance in such applications depends not only on particle size, size distribution, and morphology, but also on surface chemistry, i.e. the total number of surface functional groups (FG) and the number of FG accessible for subsequent functionalization with ligands or biomolecules, which in turn determines surface charge, colloidal stability, biocompatibility, and toxicity. Aiming at the development of simple, versatile, and multimodal tools for the quantification of many bioanalytically relevant FG and ligands, we investigated and compared various analytical methods commonly used for FG quantification. This includes electrochemical titration methods, dye-based optical assays, and other instrumental analytical techniques such as nuclear magnetic resonance and thermal analysis methods. The potential of our multimodal approach for FG quantification was demonstrated for commercial and custom-made silica particles of varying FG, showing not only an influence of the synthesis methods on the number of FG but also on the performance. In the future, our strategy can contribute to establish multi-method characterization strategies to provide a more detailed picture of the structure-properties relationship. T2 - Advanced Materials Safety 2023 CY - Saarbrücken, Germany DA - 08.11.2023 KW - Amorphous silica particles KW - Surface group analysis KW - Ligands KW - Reference material KW - Optical spectroscopy KW - Quantitative NMR KW - Optical assays KW - Titration KW - Engineered nanomaterials KW - Advanced Materials PY - 2023 AN - OPUS4-59124 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Shiflett, M. B. A1 - Magee, J. W. A1 - Tuma, Dirk ED - Shiflett, M. B. T1 - Important Developments in the History of Ionic Liquids from Academic Curiosity to Commercial Processes and Products N2 - Twenty years ago, research involving ionic liquids was a minor field of interest, and only a few chemists and even fewer engineers were interested in salts with melting points near room temperature. In April 2000, the first NATO advanced research workshop on ionic liquids was held in Heraklion, Crete. The conference was the first international meeting devoted to ionic liquids and attracted most of the active researchers at that time. Following that meeting, activity in the field began to flourish and the first books and international conferences devoted to ionic liquids began to appear. By the end of 2018, more than 80,000 scientific papers had been published, and 17,000 patents were applied for in the field of ionic liquids! This book provides an overview of the current and emerging industrial applications of ionic liquids covering the core processes and products, the practical implementation and technical challenges involved, and the potential future directions for Research and development. The individual chapters were written by leading scientists in the field from industry and academia to address specific processes and products that are or will be soon commercialized. Examples include the use of a chloroaluminate ionic liquid as a next-generation alkylation catalyst to a new class of capillary gas chromatography (GC) columns with stationary phases based on ionic liquids. Over the past twenty years, there has been a growing realization that ionic liquids have moved from being mere academic curiosities to having genuine applications in fields as wide-ranging as advanced materials, biotechnology, catalysis, pharmaceuticals, renewable fuels, and sustainable energy. There are many optimistic indications that ionic liquids are on their way to becoming a commercial success story. This first book on “Commercial Applications of Ionic Liquids” provides over 50 applications that are either at the pilot scale or have been commercialized, which indicates that an exciting new chapter in the field of ionic liquids is about to begin! KW - Ionic Liquids KW - Historical Developments KW - Advanced Materials PY - 2020 SN - 978-3-030-35245-5 DO - https://doi.org/10.1007/978-3-030-35245-5_1 SN - 2196-6990 SP - 3 EP - 29 PB - Springer CY - Cham AN - OPUS4-50453 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Rühle, Bastian T1 - Perspektive der BAM auf neuartige Materialien N2 - Neuartige Materialien, die bekannte (Werk-)Stoffe mit neuen Funktionalitäten ausstatten, spielen eine zunehmend wichtige Rolle im Bereich der Materialforschung und -prüfung. Das Spektrum neuartiger Materialien reicht von der gezielten Oberflächenfunktionalisierung und -strukturierung makroskopischer Materialien, dünnen Beschichtungen bis hin zu mikro- und nanoskaligen Kompositmaterialien und funktionalen Materialien an der Schnittstelle zur Biologie, Biotechnologie, nachhaltige Energiespeicherung und Sensorik. Dabei bieten neuartige Materialien die Chance, Werkstoffe und Produkte mit erweiterter oder verbesserter Funktionalität zu erhalten und Sicherheit bereits im Designprozess zu berücksichtigen. Durch dieses breite Anwendungsspektrum und die Herausforderungen, die solche Materialien für die Sicherheit in Chemie und Technik mit sich bringen, sind diese in allen Themenfeldern der BAM repräsentiert (Material, Analytical Sciences, Energie, Infrastruktur und Umwelt). Die Aufgaben der BAM erstrecken sich dabei von der Herstellung von Referenzmaterialien für Industrie, Forschung und Regulation, über die Erstellung von standardisierten Referenzverfahren für nachhaltige Messungen im Umwelt- und Lebenswissenschaftsbereich bis hin zur Bereitstellung von belastbaren und zitierbaren Referenzdaten. Durch die genaue Charakterisierung neuartiger Materialien können potentiell problematische Substanzen identifiziert und deren Risiken besser abgeschätzt werden. In diesem Beitrag werden einige aktuelle Beispiele aus diesen Bereichen vorgestellt. T2 - NanoDialog der Bundesregierung - Chancen und Risiken von Neuartigen Materialien CY - Berlin, Germany DA - 22.05.2019 KW - Advanced Materials KW - Nanomaterialien KW - Neuartige Materialien PY - 2019 AN - OPUS4-49594 LA - deu 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 - 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 - 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 - 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 -