TY - JOUR A1 - Deumer, J. A1 - Pauw, Brian Richard A1 - Marguet, S. A1 - Skroblin, D. A1 - Taché, O. A1 - Krumrey, M. A1 - Gollwitzer, C. T1 - Small-angle X-ray scattering: characterization of cubic Au nanoparticles using Debye’s scattering formula N2 - A versatile software package in the form of a Python extension, named CDEF (computing Debye’s scattering formula for extraordinary form factors), is proposed to calculate approximate scattering profiles of arbitrarily shaped nanoparticles for small-angle X-ray scattering (SAXS). CDEF generates a quasi-randomly distributed point cloud in the desired particle shape and then applies the open-source software DEBYER for efficient evaluation of Debye’s scattering formula to calculate the SAXS pattern (https://github.com/j-from-b/CDEF). If self-correlation of the scattering signal is not omitted, the quasi-random distribution provides faster convergence compared with a true-random distribution of the scatterers, especially at higher momentum transfer. The usage of the software is demonstrated for the evaluation of scattering data of Au nanocubes with rounded edges, which were measured at the four-crystal monochromator beamline of PTB at the synchrotron radiation facility BESSY II in Berlin. The implementation is fast enough to run on a single desktop computer and perform model fits within minutes. The accuracy of the method was analyzed by comparison with analytically known form factors and verified with another implementation, the SPONGE, based on a similar principle with fewer approximations. Additionally, the SPONGE coupled to McSAS3 allows one to retrieve information on the uncertainty of the size distribution using a Monte Carlo uncertainty estimation algorithm. KW - X-ray scattering KW - SAXS KW - Non-spherical nanoparticles KW - Nanoparticles KW - Nanomaterials KW - Debye scattering equation KW - Simulation KW - Data fitting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-557652 DO - https://doi.org/10.1107/S160057672200499X VL - 55 IS - Pt 4 SP - 993 EP - 1001 PB - International Union of Crystallography CY - Chester, England AN - OPUS4-55765 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard T1 - Glimpses of the future: Systematic investigations of 1200 mofs using a highly automated, full-stack materials research laboratory N2 - By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up. T2 - Winter School on Metrology and Nanomaterials for Clean Energy CY - Claviere, Italy DA - 28.01.2024 KW - Digitalization KW - Automation KW - Digital laboratory KW - Scattering KW - Synthesis KW - Nanomaterials KW - Holistic science PY - 2024 AN - OPUS4-59621 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard T1 - Basics and applications of good SAXS: Quantifying the fine structure of lots of materials N2 - In contrast to the crisp, clear images you can get from electron microscopy, small-angle X-ray scattering (SAXS) patterns are rather featureless. These patterns, however, contain averaged structural information of all of the finest material structures that were illuminated by the X-ray beam. With careful and precise investigation, and supplementary information from complementary techniques, this bulk material structure can be quantified to reveal structural information spanning four or even five decades in size. Additionally, while the data correction and analysis is complex, sample preparation is very straightforward, also allowing for in-situ and operando measurements to be performed without breaking a sweat. In the right hands, then, this technique can be the most powerful tool in your analytical arsenal. T2 - OpTecBB webinar within the scope of the focus area Optical Analytics CY - Online meeting DA - 27.05.2020 KW - Small-angle scattering KW - Introduction KW - Application KW - Saxs KW - Nanomaterials KW - Nanostructure PY - 2020 UR - https://www.youtube.com/watch?v=mXkYL3dSsTY UR - https://optecbb.de/veranstaltungen/veranstaltung/webinar-basics-and-applications-of-good-saxs-1238/ AN - OPUS4-50879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bresch, Harald T1 - Core-shell systems - different cases N2 - Coating, stabilization layers, functionalization of particles or simple contamination are common variants of a core-shell system. For smaller nanoparticles this is of major importance. A particle with 16 nm diameter and a usual surface layer of 2 nm will have the same volume for the core as for the shell. In this case the material of the particle doesn’t have a clear definition. It is a common case that a particle consists of four different layers: Core, shell, stabilization layer and contamination. The properties of the particles differ according to this structure. For example silver particles might have a different dissolution rate for pure particles and for particles which are grown on top of a core. Different solubility or defined other properties of materials is a common reason for producing core-shell systems. Gold cores are surrounded by silica to stabilize them or to get a defined distance between the cores. Silica might be surrounded by gold and the silica dissolved afterwards. This delivers hollow shells. Another important example for core-shell systems are quantum dots. A small core is surrounded by a different material for increasing the photoluminescence. Furthermore there a stabilization layer is needed. The smallest part of the final particles is the initial core. The photoluminescence is based on this core, but the shells contain much more material. Categorization should address this. Core-shell systems are not covered by most of the existing decision trees for grouping. They are either regarded as special case or a singular layer. This disqualifies core-shell systems for grouping within the common models. There might be a very easy way to avoid this problem and even to combine some of the different decision trees. Starting the decision tree with the solubility of the outer shell and subsequently addressing the inner layers will be a pragmatic approach to solve the problem. If there is no shell, the categorization can start with a tiered approach or with the proposed “stawman” chemical categorization. If a shell is covering the surface there is a need to check if the shell is stable. If it is stable, the particle can be categorized based on this shell. If it is soluble, the ions need to be addressed as in the classic case. Furthermore the shell might increase the uptake by the cells. If the ions and the uptake are not critical the categorization can continue with the next layer. With this not perfect but pragmatic approach, the surface layers can be addressed with very limited additional efforts. Most criteria are based on classically tabulated data. Including a rating system like the precautionary matrix approach might even address the fact that some parameters are not always Yes/No, e.g. solubility, ion toxicity and uptake. T2 - OECD Expert Meeting on Grouping and Read Across for the Hazard Assessment of Manufactured Nanomaterials CY - Brussels, Belgium DA - 13.04.2016 KW - OECD KW - Nanoparticles KW - Nanomaterials KW - Grouping KW - Nano PY - 2016 AN - OPUS4-35774 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 - GEN A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob T1 - Complete set of raw and processed datasets, as well as associated Jupyter notebooks for analysis, associated with manuscript entitled: "The MOUSE project: a practical approach for obtaining traceable, wide-range X-ray scattering information" N2 - This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title. In the manuscript, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for Ultrafine Structure Exploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples. KW - X-ray scattering KW - Measurement methodology KW - Traceability derivation KW - Multi-scale measurements KW - Systems architecture KW - Nanomaterials PY - 2020 DO - https://doi.org/10.5281/zenodo.4312953 PB - Zenodo CY - Geneva AN - OPUS4-51825 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Smales, Glen Jacob A1 - Pauw, Brian Richard T1 - The MOUSE project - A meticulous approach for obtaining traceable, wide-range X-ray scattering information N2 - Herein, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for UltrafineStructure Exploration). The MOUSE project provides scattering information on a wide variety of samples, with traceable dimensions for both the scattering vector (q) and the absolute scattering cross-section (I). The measurable scattering vector-range of 0.012≤ q (nm-1) ≤ 92, allows information across a hierarchy of structures with dimensions ranging from ca. 0.1 to 400 nm. In addition to details that comprise the MOUSE project, such as the organisation and traceable aspects, several representative examples are provided to demonstrate its flexibility. These include measurements on alumina membranes, the tobacco mosaic virus, and dual-source information that overcomes fluorescence limitations on ZIF-8 and iron-oxide-containing carbon catalyst materials. KW - X-ray scattering KW - MOUSE KW - Instrumentation KW - SAXS KW - Methodology KW - Traceability KW - Wide-range KW - Data curation KW - FAIR KW - Uncertainties KW - Nanomaterials KW - Nanometrology PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-528669 DO - https://doi.org/10.1088/1748-0221/16/06/P06034 VL - 16 IS - 6 SP - 1 EP - 50 PB - IOP CY - Bristol, UK AN - OPUS4-52866 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 -