TY - JOUR A1 - Duarte Bernardino, Carolina A1 - Lee, Mihyun A1 - Ren, Qun A1 - Ruehle, Bastian T1 - Facile Spray-Coating of Antimicrobial Silica Nanoparticles for High-Touch Surface Protection N2 - The rising threat from infectious pathogens poses an ever-growing challenge. Metal-based nanomaterials have gained a great deal of attention as active components in antimicrobial coatings. Here, we report on the development of readily deployable, sprayable antimicrobial surface coatings for high-touch stainless steel surfaces that are ubiquitous in many healthcare facilities to combat the spread of pathogens. We synthesized mesoporous silica nanoparticles (MSNs) with different surface functional groups, namely, amine (MSN-NH2), carboxy (MSN-COOH), and thiol groups (MSN-SH). These were chosen specifically due to their high affinity to copper and silver ions, which were used as antimicrobial payloads and could be incorporated into the mesoporous structure through favorable host−guest interactions, allowing us to find the most favorable combinations to achieve antimicrobial efficacy against various microbes on dry or semidry high-touch surfaces. The antimicrobial MSNs were firmly immobilized on stainless steel through a simple two-step spray-coating process. First, the stainless steel surfaces are primed with sprayable polyelectrolyte solutions acting as adhesion layers, and then, the loaded nanoparticle dispersions are spray-coated on top. The employed polyelectrolytes were selected and functionalized specifically to adhere well to stainless steel substrates while at the same time being complementary to the MSN surface groups to enhance the adhesion, wettability, homogeneity, and stability of the coatings. The antimicrobial properties of the nanoparticle suspension and the coatings were tested against three commonly found pathogenic bacteria, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, as well as a fungal pathogen, Candida albicans. Especially MSN-SH loaded with silver ions showed excellent antimicrobial efficacy against all tested pathogens under application-relevant, (semi)dry conditions. The findings obtained here facilitate our understanding of the correlation between the surface properties, payloads, and antimicrobial activity and show a new pathway toward simple and easily deployable solutions to combat the spread of pathogens with the help of sprayable antimicrobial surface coatings. KW - Mesoporous silica nanoparticles KW - Thin films KW - Antimicrobial coatings KW - Spray-coating KW - Infectious diseases KW - Pathogen transmission KW - High-touch surfaces PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626269 DO - https://doi.org/10.1021/acsami.4c18916 SN - 1944-8252 SP - 1 EP - 13 PB - ACS Publications CY - Washington, DC AN - OPUS4-62626 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 - Schmitt, Johannes T1 - Data acquisition system for single particle inductively coupled plasma mass spectrometry (spICP-MS) with nanosecond time resolution N2 - This study presents our data acquisition system prototype for single particle inductively coupled plasma mass spectrometry (spICP-MS) with nanosecond time resolution (nanoDAQ) and a matching data processing approach for time-resolved data in the nanosecond range. The system continuously samples the secondary electron multiplier (SEM) detector signal with a dwell time of approximately 2 ns and enables detection of gold nanoparticles (AuNP) as small as 7.5 nm with a commercial single quadrupole ICP-MS instrument. [1] Analysis of acquired transient data is based on the temporal distance between detector events and a derived ion event density. It was shown that the inverse logarithm of the distance between detector events is proportional to particle size. Also, the number of detector events per particle can be used to calibrate and determine the particle number concentration (PNC) of a nanoparticle dispersion. Particle-by-particle-based analysis of ion event density and other parameters derived from nanosecond time resolution show promising results. High data acquisition frequency of the systems allows recording of a statistically significant number of data points in 60 s or less, which leaves only the sample uptake and rinsing steps as remaining factors for limiting the total measurement time. T2 - 20th European Winter Conference on Plasma Spectrochemistry CY - Berlin, Germany DA - 02.03.2025 KW - ICP-MS KW - Instrumentation KW - Nano KW - Nanoparticle Characterization PY - 2025 AN - OPUS4-63599 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 - JOUR A1 - Vermeeren, Sarah A1 - Witzler, Markus A1 - Makarow, Ramona A1 - Engelhard, Carsten A1 - Kaul, Peter T1 - Multivariate evaluation method for the detection of pest infestations on plants via VOC analysis using gas chromatography mass spectrometry N2 - Volatile organic compounds (VOCs) play an important role in the defense against pest infestations on plants. The analysis of these VOCs using gas chromatography mass spectrometry (GC-MS) enables the detection of pests by analyzing the VOC composition (VOC profiles) for specific patterns and markers. The analysis of such complex datasets with high biovariability poses a particular challenge. For this reason, a multivariate evaluation method based on a self-written Python script, using principal component analysis (PCA) and linear discriminant analysis (LDA), was developed and tested for functionality using a dataset, which has been evaluated manually and has identified five specific markers (2,4-dimethyl-1-heptene, 3-carene, alpha-longipinene, cyclosativene, and copaene) for Anoplophora glabripennis (ALB) infestation on Acer trees. The results obtained in the present study did not only match the manually evaluated results, but lead to further insight into the dataset. Another sesquiterpene which is assumed to be alpha-zingiberene was identified as an ALB specific marker in addition to 2,4-dimethyl-1-heptene and 3-carene. Furthermore, the European native beetle species goat moth Cossus cossus (CC) and poplar long-horned beetle Saperda carcharias (SC) were also analyzed for their VOCs to differentiate ALB specific VOC from other pest infestations. This comparison lead to the conclusion that the compounds alpha-longipinene, cyclosativene, and copaene are not specific for ALB but for pest infestation in general. It was possible to identify not only specifically produced VOCs, but also differences in concentrations that arise specifically during ALB infestation. Therefore, the evaluation method for the detection of plant pests presented in this study represents a time-saving alternative to conventional non computing methods, which in addition provides more detailed results. KW - Mass Spectrometry KW - Gas Chromatography KW - Volatile organic compounds KW - Pest infestation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639526 DO - https://doi.org/10.1038/s41598-025-11607-5 SN - 2045-2322 VL - 15 IS - 1 SP - 1 EP - 10 PB - Springer Science and Business Media LLC AN - OPUS4-63952 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Braun, Jennifer A1 - Engelhard, Carsten A1 - Kaul, Peter T1 - Optimized fast gas chromatography coupled with proton-transfer-reaction time-of-flight mass spectrometry for the selective near real-time analysis of herbivore-induced plant volatiles N2 - The analysis of herbivore-induced plant volatiles (HIPVs) is essential for understanding plant-environment interactions and defense strategies against herbivores. Proton transfer reaction time-of-flight mass spectrometry (PTR–TOF–MS) is a powerful analytical tool that enables real-time monitoring and quantification of diverse groups of HIPVs. However, the PTR–TOF–MS technique is constrained in its ability to effectively differentiate between isomers. When analyzing complex mixtures of HIPVs, the separation of isomers becomes crucial as major compound classes such as terpenes comprise thousands of isomers. In this study, we present an optimized fast gas chromatography (fastGC) based on a modified version of the commercially available fastGC add-on integrated into a mobile PTR–TOF. The system was optimized for the analysis of emissions from enclosed trunks of Acer platanoides infested by Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB). The development of fastGC was primarily focused on the sesquiterpenes α-longipinene, cyclosativene and α-copaene, which serve as strong indicators of ALB infestation. These sesquiterpenes were separated in less than three minutes, with intra-day retention time RSD < 0.6 % and resolutions of 2.6 ± 0.3 and 1.3 ± 0.2. In comparison to the original system, the optimized fastGC demonstrates more than tripled sesquiterpene resolution, twice the sensitivity relative to direct inlet mode, and an approximately 10 % reduction in total analysis time. The optimized fastGC–PTR–TOF allows for near real-time analysis of complex mixtures of biogenic VOCs, making it a powerful tool for environmental monitoring, integrated pest management, and forest protection. KW - Mass Spectrometry KW - PTR-TOF-MS KW - Gas Chromatography KW - Herbivore-induced plant volatiles KW - Sesquiterpenes PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639513 DO - https://doi.org/10.1016/j.chroma.2025.466236 SN - 0021-9673 VL - 1759 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-63951 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Rudolf T1 - Bestimmung von Spurenstoffen im (Roh-)Abwasser mittels ELISA N2 - Der Vortrag behandelt die Bestimmung von Spurenstoffen im Rohabwasser mithilfe von ELISA (Enzyme-Linked Immunosorbent Assay), einer immunanalytischen Methode, die sich durch hohe Sensitivität und Eignung für Hochdurchsatzanalysen auszeichnet. Im Rahmen der Projekte MARKERIA I & II wurden verschiedene anthropogene Marker wie Koffein, Carbamazepin, Diclofenac, Bisphenol A und Hormone wie Östron untersucht, um deren Eignung für die Abwassersurveillance zu bewerten. Die Ergebnisse zeigen, dass einige Substanzen wie Carbamazepin stabile Konzentrationen aufweisen und sich gut als Marker eignen, während andere wie Koffein starke Schwankungen zeigen. Einige Marker wie Isolithocholsäure werden zwar in hohen Konzentrationen gefunden, zeigen aber methodische Herausforderungen wie instabile Assays. Die Studie hebt hervor, dass es Hotspots mit erhöhten Konzentrationen gibt, aber insgesamt eine relativ geringe Variabilität zwischen verschiedenen Rohabwässern besteht. Zukünftige Entwicklungen sollen sich auf die Verbesserung von Antikörpern, die Entwicklung tragbarer Vor-Ort-Analysemethoden und die Standardisierung immunanalytischer Verfahren konzentrieren. T2 - AMELAG-Colloquium "Spurenstoffe im (Ab-)Wasser" CY - Online meeting DA - 20.06.2025 KW - Antikörper KW - Schnelltests KW - Abwasser KW - ELISA PY - 2025 AN - OPUS4-63500 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Engelhard, Carsten T1 - On ICP-MS with Nanosecond Time Resolution: From Nanoparticles to Microplastics N2 - In this presentation, recent developments in inductively coupled plasma mass spectrometry (ICP-MS) instrumentation for particle characterization in complex mixtures will be reviewed. The current state-of-the-art in single-particle (sp) ICP-MS instrumentation for the detection and characterization of nanoparticles (NP) and microplastics (MPs) as well as remaining challenges will be discussed. While millisecond dwell times were used in the advent of spICP-MS, the use of microsecond dwell times helped to improve nanoparticle data quality and particle size detection limits. We could show that a custom-built high-speed data acquisition unit with microsecond time resolution (μsDAQ) can be used to successfully address issues of split-particle events and particle coincidence, to study the temporal profile of individual ion clouds, and to extend the linear dynamic range by compensating for dead time related count losses. Our latest development is an in-house built data acquisition system with nanosecond time resolution (nanoDAQ). Recording of the SEM signal by the nanoDAQ is performed on the nanosecond time scale with a dwell time of approximately 2 ns and enables detection of gold nanoparticles (AuNP) as small as 7.5 nm with a commercial single quadrupole ICP-MS instrument. [1] Analysis of acquired transient data is based on the temporal distance between detector events and a derived ion event density. It was shown that the inverse logarithm of the distance between detector events is proportional to particle size. Also, the number of detector events per particle can be used to calibrate and determine the particle number concentration (PNC) of a nanoparticle dispersion. In addition to inorganic nanoparticles, first results on the detection of microplastics with spICP-MS will be discussed. T2 - ANAKON 2025 CY - Leipzig, Germany DA - 10.03.2025 KW - Nano KW - Microplastics KW - Nanoparticle Characterization KW - ICP-MS KW - Instrumentation PY - 2025 AN - OPUS4-63580 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -