TY - JOUR A1 - Shahsanaei, Majid A1 - Farahbakhsh, Nastaran A1 - Pour-Ali, Sadegh A1 - Schardt, Annika A1 - Orangpour, Setareh A1 - Engelhard, Carsten A1 - Mohajernia, Shiva A1 - Killian, Manuela S. A1 - Hejazi, Sina T1 - Synergistic enhancement of photocatalytic hydrogen production in TiO2 nanosheets through light-induced defect formation and Pt single atoms N2 - In this investigation, we present a direct method employing UV-light radiation to induce point defects, specifically Ti3+ and VO, onto the surface of TiO2 nanosheets (TiO2-NSs) and efficiently decorate them with Pt particles. The addition of the Pt precursor is carried out during rest periods following UV-light cessation (light-induced samples, LI) and during UV-light exposure (photo-deposited samples, PD). The size and distribution of Pt particles on both LI and PD TiO2-NSs are systematically correlated with varying resting times, enabling precise control over Pt loading. The characterization of various TiO2-NSs is extensively conducted using microscopy techniques (FESEM, TEM, and HAADF-STEM) and spectroscopy (XPS). Gas chromatography is also employed for the evaluation of the H2 photocatalytic performance of various samples. Our findings reveal that Pt particles deposit on the TiO2-NSs surfaces as nanoparticles under illumination. After a 5 minutes resting time, a combination of Pt single atoms (SAs) and clusters, with a maximum loading of 0.37 at%, is formed. Extending the resting time to 60 minutes results in a gradual reduction in Pt SAs and clusters, leading to the deposition of Pt nanoparticles with lower loadings. Notably, Pt SAs and clusters exhibit superior performance in hydrogen evolution, showcasing a remarkable 4000-fold increase over pristine TiO2-NSs. Additionally, sustained UV radiation during Pt addition in the photo-deposited samples results in the formation of Pt nanoparticles with lower loading compared to LI samples, consequently diminishing photocatalytic hydrogen production. This study not only provides insights into the controlled manipulation of Pt SAs on TiO2-NSs but also highlights their exceptional efficacy in hydrogen evolution, offering valuable contributions to the design of efficient photocatalytic systems for sustainable hydrogen generation. KW - Chemistry KW - Nanosheets KW - Hydrogen PY - 2024 DO - https://doi.org/10.1039/D4TA01809E VL - 12 IS - 29 SP - 18554 EP - 18562 PB - Royal Society of Chemistry (RSC) AN - OPUS4-61271 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - 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 - Bresch, Harald T1 - Anwendungen von maschinellem Lernen und KI an der BAM N2 - Im Austausch mit den anderen Bundesoberbehörden wurden die KI-Ansätze der verschiedenen Bundesoberbehörden zu spezifischen Themen der Nanowissenschaften präsentiert. Der Vortrag der BAM fokusiert sich auf die Themen "Self driving lab", semantische Segmentierung und Auswertung von elektronenmikroskopischen Bildern sowie die Generierung von ausführbaren Machineninstruktionen aus natürlicher Sprache. Abschließend wird der neue BAM DataStore vorgestellt. T2 - Nano-Behördenklausur 2024 CY - Berlin, Germany DA - 03.07.02024 KW - Nano KW - Bundesoberbehörden KW - Künstliche Intelligenz KW - Neuronale Netzwerke KW - Elektronisches Laborbuch PY - 2024 AN - OPUS4-61819 LA - deu 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 - 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 - JOUR A1 - Chen, Xinyue A1 - Dong, Ximan A1 - Zhang, Chuyan A1 - Zhu, Meng A1 - Ahmed, Essraa A1 - Krishnamurthy, Giridharan A1 - Rouzbahani, Rozita A1 - Pobedinskas, Paulius A1 - Gauquelin, Nicolas A1 - Jannis, Daen A1 - Kaur, Kawaljit A1 - Hafez, Aly Mohamed Elsayed A1 - Thiel, Felix A1 - Bornemann, Rainer A1 - Engelhard, Carsten A1 - Schönherr, Holger A1 - Verbeeck, Johan A1 - Haenen, Ken A1 - Jiang, Xin A1 - Yang, Nianjun T1 - Interlayer Affected Diamond Electrochemistry N2 - Diamond electrochemistry is primarily influenced by quantities of sp3‐carbon, surface terminations, and crystalline structure. In this work, a new dimension is introduced by investigating the effect of using substrate‐interlayers for diamond growth. Boron and nitrogen co‐doped nanocrystalline diamond (BNDD) films are grown on Si substrate without and with Ti and Ta as interlayers, named BNDD/Si, BNDD/Ti/Si, and BNDD/Ta/Ti/Si, respectively. After detailed characterization using microscopies, spectroscopies, electrochemical techniques, and density functional theory simulations, the relationship of composition, interfacial structure, charge transport, and electrochemical properties of the interface between diamond and metal is investigated. The BNDD/Ta/Ti/Si electrodes exhibit faster electron transfer processes than the other two diamond electrodes. The interlayer thus determines the intrinsic activity and reaction kinetics. The reduction in their barrier widths can be attributed to the formation of TaC, which facilitates carrier tunneling, and simultaneously increases the concentration of electrically active defects. As a case study, the BNDD/Ta/Ti/Si electrode is further employed to assemble a redox‐electrolyte‐based supercapacitor device with enhanced performance. In summary, the study not only sheds light on the intricate relationship between interlayer composition, charge transfer, and electrochemical performance but also demonstrates the potential of tailored interlayer design to unlock new capabilities in diamond‐based electrochemical devices. KW - Nanocrystalline diamond KW - Interfaces KW - Electrochemistry KW - TOF-SIMS KW - SEM PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621576 DO - https://doi.org/10.1002/smtd.202301774 SN - 2366-9608 VL - 9 IS - 2 SP - 1 EP - 10 PB - Wiley VHC-Verlag CY - Weinheim AN - OPUS4-62157 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 - 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 that can be analyzed using Raman and FTIR spectroscopy combined with optical microscopy. Additionally, pyrolysis (Py) or thermal extraction and desorption (TED) coupled with gas chromatography-mass spectrometry (GC-MS) are used for the characterization of MPs.[1] However, sample throughput is limited due to GC separation. This work presents a feasibility study for the direct, rapid analysis of MPs using high-resolution mass spectrometry and a plasma-based ambient desorption/ionization source (FAPA, flowing atmospheric-pressure afterglow).[2] In earlier work, an in-house built ambient ionization source (modelled after the pin-to-capillary (p2c) FAPA design by Shelley et al. [3]) was coupled to HR-Orbitrap MS and used to characterize different MPs produced in-house from plastic materials including 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 presents an improved desorption/ionization approach using higher temperatures for desorption enhancement (approximately 500 °C, achieved with economical and commercially available parts) and a tailored source housing combined with a halo-shaped (h-FAPA) source configuration.[4] The scope was expanded to include MPs from poly(ethylene terephthalate) (PET), poly(methyl methacrylate) (PMMA), and poly(vinyl chloride) (PVC), sized 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 - ANAKON 2025 CY - Leipzig, Germany DA - 10.03.2025 KW - Mass Spectrometry KW - Plasma KW - FAPA-MS KW - Microplastics PY - 2025 AN - OPUS4-63604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schütz, Désirée A.-M. T1 - Feasibility Study on the Adsorption of Environmental Contaminants onto Fresh and Aged Microplastics N2 - Microplastics (MPs) refer to plastic particles, fibers, or beads with sizes ranging from 100 nm to 5 mm in size. Their pervasive distribution as environmental contaminants have escalated into a significant global concern. Primary MPs are intentionally produced particles for industrial and commercial applications, such as exfoliants in personal care and cosmetic formulations. In contrast, secondary MPs are generated through the fragmentation and degradation of larger plastic materials, due to environmental weathering processes. [1] Due to their high surface area-to-volume ratio and hydrophobic nature, MPs have the potential to serve as vectors for the accumulation and transport of diverse organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), perfluoroalkyl substances (PFAS), pharmaceuticals and personal care products (PPCPs), as well as trace metals such as silver, cadmium, chromium, and copper. In the environmental, MPs are subject to aging processes driven by factors such as temperature, ultraviolet radiation, oxygen, and chemical interactions with environmental toxins. This aging can induce significant alterations in their physicochemical properties, which, in turn, can affect the adsorption behavior. [2] Classical and alternative analytical methods such as high-performance liquid chromatography (HPLC) and ambient desorption/ionization high-resolution mass spectrometry (ADI-HR-MS) can help to study the adsorption potential. ADI-HR-MS allows rapid sample analysis with minimal preparation, providing results in under a minute, much faster than traditional chromatographic techniques. [3] This study aims to investigate the influence of aging and particle size on the ability of microplastics to act as vectors for environmental contaminants. Microplastics were prepared in-house and subjected to controlled aging conditions for 12, 24, 36, and 48 hours. Subsequently, the aged MPs were exposed in plastic-free containers to model solutions to simulate co-contaminants in the environment. Adsorption onto synthesized secondary microplastics, varying in size and aging status, was investigated at different time intervals with ambient MS and time-of-flight secondary ion mass spectrometry. T2 - ANAKON 2025 CY - Leipzig, Germany DA - 10.03.2025 KW - Mass Spectrometry KW - FAPA-MS KW - Microplastics KW - TOF-SIMS KW - Surface Analysis PY - 2025 AN - OPUS4-63606 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. [1] 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 - ANAKON 2025 CY - Leipzig, Germany DA - 10.03.2025 KW - ICP-MS KW - Instrumentation KW - Nano KW - Nanoparticle Characterization PY - 2025 AN - OPUS4-63603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -