TY - CONF A1 - Kozdras, Mark T1 - Towards a Self-driving Lab for Nanoparticle Research N2 - Society is currently confronted with two global challenges, climate change and sustainable development. This reality reverberates amongst the leading nations of the world and is articulated as a priority by the United Nations through the Framework Convention on Climate Change and its seventeen Sustainable Development Goals. In 2016, under the Paris Accord, Mission Innovation, MI, emerged as a global response to climate change and developed eight innovation challenges to mitigate its effect, including Clean Energy Materials, IC6. This innovation challenge focused its efforts on accelerating the development and deployment of clean energy materials by more than a factor of ten through Materials Acceleration Platforms, MAPs – autonomous, self-driving materials laboratories and renewed itself under the current mandate as Materials for Energy, M4E. Self-driving labs deploy artificial intelligence, robotic automation and high-performance simulation and modeling in a closed loop system of material synthesis and characterization. An international ecosystem for accelerated materials discovery has been established and finds applications in many enabling materials technologies, including nanomaterials. The importance of nanomaterials to catalysis for hydrogen production and carbon dioxide conversion as well as energy storage in batteries is well known. In this work, the international efforts under Materials for Energy will be elaborated including the development of MINERVA - MAP for Intelligent Nanomaterial synthesis Enabled by Robotics for Versatile Applications. MINERVA was specifically built to include the specialized equipment required for the synthesis, characterization and closed-loop optimization of various nano- and advanced materials, ranging from simple inorganic (silica, metal, metal oxide) or polymeric nanoparticles to more complex core-shell architectures and materials with well-defined porosity or surface chemistry. Currently, we are investigating materials for applications in antimicrobial and antibiofouling surface coatings, sensor materials, as well as the reproducible synthesis of reference materials with this platform. T2 - Nanotek 2024 CY - Barcelona, Spain DA - 25.03.2024 KW - Self-driving Labs KW - SDLs KW - Advanced Materials KW - Autonomous Materials Discovery KW - Nanoparticles Synthesis and Characterization PY - 2024 AN - OPUS4-60382 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Duarte Bernardino, Carolina T1 - Metal-Ion Loaded Silica Nanoparticles as Antimicrobial Coatings for Safer High-Touch Surfaces N2 - Not only since the Covid-19 pandemic have researchers focused their efforts on high touch surfaces to minimize the contraction of infectious diseases due to human contact. To help prevent the spread of infectious pathogens, surfaces and coatings are designed to minimize the presence or survivability of pathogens on surfaces in various settings, including healthcare centers, long-term care facilities, public transport, schools, and businesses. Extensive research has focused on finding solutions to prevent bacterial transmission and biofilm formation by killing or reducing the attachment of microbes. These solutions include surface-bound active antimicrobials, biocidal coatings, and passive pathogen-repellent surfaces, developed using nanomaterials, chemical modifications, and micro- and nano-structuring. Nanomaterials are a prime candidate for such a solution. Here, we developed mesoporous silica nanoparticles (MSNs) loaded with antimicrobially active silver and copper ions that can be used in sprayable formulations as surface coatings. The influence of different surface functionalization and metal ion loadings on the efficacy of these sprayable coatings was studied. Amine- (MSN-NH2), carboxy- (MSN-COOH) and thiol-functionalized mesoporous silica nanoparticles (MSN-SH) were synthesized and characterized using different techniques, such as transmission electron microscopy (TEM), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), dynamic light scattering (DLS), electrophoretic light scattering (Zeta potential measurements) and nitrogen sorption measurements. After loading MSNs with antimicrobially active silver or copper ions, the nanoparticle dispersions were spray-coated on stainless steel substrates that were primed with sprayable polyelectrolyte solutions to enhance coating homogeneity and nanoparticle adhesion. The metal ion release was analyzed by Inductively coupled plasma optical emission spectroscopy (ICP-OES). 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. The toxicity of the coatings against human skin cells was also assessed. T2 - STOP Antimicrobial Coatings Conference CY - Mons, Belgium DA - 05.12.2024 KW - Mesoporous Silica Nanoparticles KW - Antimicrobial Coatings KW - Spray-Coating KW - Pathogens PY - 2024 AN - OPUS4-62180 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 - OPUS4-63934 LA - eng 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 - TY - CONF A1 - Engelhard, Carsten T1 - From Particles to PFAS: Recent Advances in Plasma-based Instrumentation Development N2 - In this presentation, recent advances in plasma spectrochemistry with hot and cold plasma sources for the direct detection of nanoparticles as well as per- and polyfluoroalkyl substances (PFAS) will be discussed. In the first part, single-particle inductively coupled plasma mass spectrometry (spICP-MS) with an in-house built data acquisition system with nanosecond time resolution (nanoDAQ) will be presented. In the second part, we turn to a cooler plasma source. Specifically, a flowing atmospheric-pressure afterglow source (FAPA) and its application for the direct mass spectrometric analysis of PFAS will be discussed. 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 KW - PFAS PY - 2025 AN - OPUS4-63581 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schardt, Annika T1 - Fast screening method for nanoparticles in surface waters via nanosecond spICP-MS and a tailored automated ion cloud recognition algorithm N2 - Single particle inductively coupled plasma mass spectrometry (spICP-MS) is a powerful technique for nanoparticle (NP) analysis in aqueous samples, which provides essential information on size distribution and particle number concentration (PNC) of nanometer-sized particles in various water samples for risk assessment and toxicity tests. In contrast to spectroscopic particle analysis methods, this mass spectrometry-based tool can provide chemical information on the elemental composition of NPs after minimal sample preparation. We recently presented a novel spICP-MS instrumentation and tailored software that acquires data with nanosecond time resolution, lowering the particle size detection limit to 7 nm for gold NP (1). The system directly samples the output signal of the electron multiplier and records the detection of individual ions with a time resolution of only a few nanoseconds. With nanosecond time resolution, we were able to visualize profiles of ion clouds that were produced from ionization of nanoparticles in the ICP on a single-ion basis and to use the temporal gap between those ions for particle sizing. Our latest improvement of the data acquisition system (nanoDAQ) features ca. 2 ns integration time and a matching processing software prototype, which automatically recognizes and counts ion clouds in the transient data. With this combination we achieved an experimentally determined size detection limit of ca. 5 nm for gold nanoparticles. A feasibility study shows that the nanoDAQ in combination with the ion cloud recognition algorithm succeeds in fast detection and counting of NP containing Ag, Ce, or Zr in waste water and surface water samples from the area of Siegen. PNCs ranged from ca. 7 x 106–2 x 108 particles/L, which is in good agreement with concentrations reported for similar water samples in the literature. T2 - 56th Annual Conference of the German Society for Mass Spectrometry (DGMS) CY - Göttingen, Germany DA - 04.03.2025 KW - Instrumentation KW - Mass Spectrometry KW - Nanoparticles KW - spICP-MS PY - 2025 AN - OPUS4-63662 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Escobar-Carranza, Cristian C. T1 - On the Detection of Microplastics by Flowing Atmospheric-Pressure Afterglow Mass Spectrometry (FAPA-MS) N2 - Microplastics (MPs) are widespread pollutant particles analyzed using Raman and FTIR spectroscopy combined with optical microscopy. Pyrolysis (Py) or thermal extraction and desorption (TED) coupled with gas chromatography-mass spectrometry (GC-MS) are used for the characterization of MPs, though GC limits sample throughput. This work explores direct, rapid MP analysis using high-resolution (HR) MS and a plasma-based ambient desorption/ionization source (FAPA, flowing atmospheric-pressure afterglow). Previously, an in-house pin-to-capillary (p2c) FAPA source coupled to HRMS characterized MPs made in-house from polystyrene (PS), polypropylene (PP), low-density polyethylene (LDPE), and polycarbonate (PC). Simultaneous detection of characteristic ions and particle imaging on a sampling mesh was feasible, with detection limits (LOD) for PS MPs at 311 µm in size and 1.3 mg in mass. Principal component analysis (PCA) was used for particle differentiation. This work introduces a high-temperature desorption method (~500 °C) with economical and commercially available parts and a tailored housing combined with a halo-shaped (h-FAPA) source configuration. The study expands to include poly(ethylene terephthalate) (PET), poly(methyl methacrylate) (PMMA), and poly(vinyl chloride) (PVC) MPs (125–250 µm). Data visualization and interpretation were performed using Kendrick mass defect plots and other multivariate analysis tools. Compared to earlier results, h-FAPA-MS yielded at least 65% higher ion signals for selected ions in all MPs. These ions were detected mainly as protonated species [M+H]+. Higher thermal desorption temperatures aided in detecting all MPs, as the presence of higher molecular weight fragments added specificity to the analysis. Notably, experiments with the h-FAPA source demonstrated lower mass-based LODs for MPs than the p2c-FAPA source (e.g., 14 µg vs 1.3 mg for PS, respectively). T2 - 56th Annual Conference of the German Society for Mass Spectrometry (DGMS) CY - Göttingen, Germany DA - 04.03.2025 KW - Instrumentation KW - Mass Spectrometry KW - FAPA-MS KW - Microplastics PY - 2025 AN - OPUS4-63663 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - The potential of Material Acceleration Platforms (MAPs) for creating resilient and sustainable technology value chains N2 - Material Acceleration Platforms (MAPs) represent a transformative approach to the development of resilient and sustainable technology value chains. These platforms can identify candidate chemistries and structures via simulations, and database searches and leverage machine learning-based rapid screening to accelerate the discovery and deployment of novel materials, thereby addressing critical challenges in modern technology sectors. Incorporating high-fidelity advanced characterization in the early phases of material development is crucial for early de-risking. Advanced characterization techniques, such as X-ray diffraction, advanced electrochemical and spectroscopic techniques provide comprehensive insights into the structural, chemical, and physical properties of materials. Long-term testing further contributes to the de-risking process by evaluating the durability and stability of materials under various environmental and operational conditions. Early identification of potential degradation mechanisms enables the refinement of material compositions and processing methods, ultimately leading to the development of more resilient materials. Early upscaling attempts are integral to assessing the feasibility of material leads generated through machine learning-based rapid screening to evaluate the scalability of synthesis and processing techniques. This step is critical for identifying potential challenges in manufacturing, such as issues related to reproducibility, yield, and cost-effectiveness. Process design has to be a major part of the MAP-based material design to cope with the increasing share of secondary raw materials in supply chains. This presentation will briefly summarize possible strategies to address these issues and provide deep-dives on best practices. As the demand for advanced materials continues to grow, MAPs will play an increasingly vital role in driving technological advancements and addressing global challenges. T2 - Materials Week Cyprus 2024 CY - Limassol, Cyprus DA - 17.06.2024 KW - MAPs KW - SDLs KW - Sustainability PY - 2024 AN - OPUS4-60379 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano and Advanced Materials Synthesis N2 - Nano- and advanced materials have been recognized as a key enabling technology of the 21st century, due to their high potential of driving innovations in new clean energy technologies, sustainable manufacturing by substitution of critical raw materials and replacement of hazardous substances, breakthroughs in energy conversion and storage, improvement of the environmental performance of products and processes, and facilitation of circularity. Consequently, new tools that enhance the development and optimization cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future. T2 - MRS Fall Meeting 2025 CY - Boston, MA, USA DA - 30.11.2025 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation PY - 2025 AN - OPUS4-65129 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hodoroaba, Vasile-Dan T1 - Competence Center nano@BAM Welcomes ISO/TC 229 Meeting in Berlin N2 - The Competence Center nano@BAM is presented. Examples directly related to the activities of the ISO Technical Committee TC 229 Nanotechnologies as well as BAM projects on nano reference measurement procedures, nano reference materials and nano reference data sets are showed. T2 - The 32nd ISO/TC 229 IEC/TC 113 JWG2 General Meeting CY - Berlin, Germany DA - 06.11.2023 KW - ISO/TC 229 Nanotechnologies KW - Nanoparticles KW - Nano@BAM KW - Reference materials KW - Reference data KW - Reference procedures PY - 2023 AN - OPUS4-58814 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mrkwitschka, Paul T1 - Correlative analysis with electron microscopy applied in different operating modes (SEM, STEM-in-SEM and TEM) for the accurate morphological characterisation of non-spherical fine nanoparticles N2 - Electron microscopy applied in different operating modes, e.g., SEM, TEM or STEM-in-SEM, is the gold standard method to investigate the exact size and shape of individual nanoparticles. However, when fine nanoparticles with a non-monodisperse size distribution and non-spherical shapes are analysed, achieving an accurate result is challenging. Deviations in size measurements of more than 10% may occur. Understanding of the contrasts and sensitivities characteristic to the individual operating modes of an electron microscope is key in interpreting and evaluating quantitatively the measurement uncertainties needed for an eventual certification of specific nanoparticles via traceable results. Further, beyond the pure measurement, the other components in the analysis workflow with significant impact on the overall measurement uncertainties are the sample preparation and the image segmentation. In the present study the same areas of selected iron oxide fine nanoparticles (<25 nm) as reference nanomaterial (candidate) prepared on substrate for electron microscopy imaging are analysed correlatively with SEM, STEM-in-SEM and TEM with respect to their size and shape distribution. Individual significant measurement uncertainties are discussed, e.g., the sensitivity of secondary electron detectors of InLens-type to the surface morphology, particularly to the presence of an ultrathin organic coating or signal saturation effects on the particle edges, to electron beam exposure, to surface contamination, or the selection of the threshold for image segmentation. Another goal of this study is to establish a basis of analysis conditions which shall guarantee accurate results when both manual and particularly (semi-)automated segmentation approaches are applied. Advantages as well as limitations of the use of different electron microscopy operating modes, applied individually and correlatively, are highlighted. T2 - E-MRS 2024 Spring Meeting CY - Strasbourg, France DA - 27.05.2024 KW - Nanoparticles KW - Electron Microscopy KW - Metrology KW - Imaging KW - Reference materials PY - 2024 AN - OPUS4-60436 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bernardino, Carolina T1 - Effortless Antimicrobial Shield: Spray-coated Silica Nanoparticles For Safer High-touch Surfaces N2 - Functional films with tailored interfacial properties play a pivotal role for the development of next generation surface coatings, particularly in healthcare-related environments. In this contribution, we present a facile spray-coating method for the creation of antimicrobial thin films on high-touch surfaces using mesoporous silica nanoparticles (MSNs) that were specifically functionalized to enable strong adhesion and sustained release of metal-based antimicrobial agents. The process is scalable and addresses key challenges in adhesion control, film homogeneity, and long-term antimicrobial function against a large range of key pathogens responsible for nosocomial infections. Three distinct types of MSNs – bearing amine (MSN-NH₂), carboxy (MSN-COOH), and thiol (MSN-SH) surface groups – were synthesized to optimize both metal ion loading and interactions with polyelectrolyte-based adhesion layers. These surface modifications not only provide chemical handles for Cu²⁺ and Ag⁺ ion coordination but also modulate nanoparticle-substrate interactions and dispersion behavior during film formation. The coating architecture consists of a two-step process: first, spray deposition of polyelectrolyte primers that anchor strongly to stainless steel substrates; second, a nanoparticle layer that bonds electrostatically and chemically to the primer, forming robust films with great surface coverage. The films were characterized to assess structural integrity, adhesion, and functional performance. Transmission electron microscopy (TEM) and N₂ sorption analysis confirmed the mesoporous structure. ATR-FTIR and zeta potential measurements validated surface functionalization and colloidal stability. Environmental SEM revealed conformal coating across the stainless-steel surfaces with uniform nanoparticle distribution. The coating's adhesion strength was maintained through mechanical wiping and simulated wear and abrasion tests, demonstrating film durability relevant in real-world use scenarios. Antimicrobial testing under semi-dry, application-relevant conditions showed excellent performance for Ag⁺-loaded MSN-SH films, inhibiting growth of Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans. These results highlight the synergistic role of surface chemistry, metal ion loading, and film-substrate adhesion in creating effective and wear-resistant functional coatings. Moreover, these films do not show any cytotoxic properties towards Human Dermal Fibroblasts (HDF). This study contributes new insights into the design of multifunctional films where adhesion, surface functionality, and scalable processing are co-optimized for enhanced performance and shows how combining tailored surface chemistry and wide-ranging antimicrobial activity brings together smart material design for practical and safe use. T2 - MRS Fall Meeting 2025 CY - Boston, MA, USA DA - 30.11.2025 KW - Mesoporous silica nanoparticles KW - Silver KW - Antimicrobial KW - Coatings KW - Thin film PY - 2025 AN - OPUS4-65150 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -