6.1 Oberflächen- und Dünnschichtanalyse
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Regulatory decisions require reliable data and knowledge derived from this. Among stakeholders in nanotechnology, however, there is often uncertainty about the quality of data for regulatory purposes. In addition, the general public often finds itself excluded from nanoregulation and policy decisions. This creates uncertainty in the nanotechnology field and also in other branches of technology and leads to concerns among the society.
To address these issues, NANORIGO elaborates a framework to support decision making as well as data, information and knowledge sharing and use. We refer to “reliability” of data and knowledge as a degree of readiness or maturity. According to these criteria we worked out a 9-level scale in analogy to TRL (technology readiness level), the KaRL system (Knowledge, Data and Information Readiness Level). KaRL allows assessment of knowledge readiness for decision making by applying defined quality criteria for each level. It also provides guidance on how to enhance the readiness level by the help of available tools and procedures. KaRL addresses SEIN[1] principles, circular economy and thus involves the public concerns in regulation. A specialized nanorisk governance council (being under development in NANORIGO) is suggested to perform quality check of an actionable document, thus, aiding in consensus on the reliability (maturity) of knowledge for decision making. Moreover, KaRL facilitates traceability of knowledge before its use in decision making. This enables the transparency demanded by all stakeholders.
Bacteria generally interact with the environment via processes involving their cell-envelope. Thus, techniques that may shed light on their surface chemistry are attractive tools for providing an understanding of bacterial interactions. One of these tools is Al Kα-excited photoelectron spectroscopy (XPS) with its estimated information depth of <10 nm. XPS-analyses of bacteria have been performed for several decades on freeze-dried specimens in order to be compatible with the vacuum in the analysis chamber of the spectrometer. A limitation of these studies has been that the freeze-drying method may collapse cell structure as well as introduce surface contaminants. However, recent developments in XPS allow for analysis of biological samples at near ambient pressure (NAP-XPS) or as frozen hydrated specimens (cryo-XPS) in vacuum. In this work, we have analyzed bacterial samples from a reference strain of the Gram-negative bacterium Pseudomonas fluorescens using both techniques. We compare the results obtained and, in general, observe good agreement between the two techniques. Furthermore, we discuss advantages and disadvantages with the two analysis approaches and the output data they provide. XPS reference data from the bacterial strain are provided, and we propose that planktonic cells of this strain (DSM 50090) are used as a reference material for surface chemical analysis of bacterial systems.
Nanomaterials may have brought many beneficial innovations with them in our daily lives and and have become indispensable for the society. However, one needs to be concerned of the risks which are still unknown and not sufficietly studied and therefore there is a need for a nanorisk governance. At the core of nanorisk governance is gathering, processing and analysing reliable data which will be used for decision making. The challenge is to assure data reliability and transform it into knowledge. To address this challenge, we used analogy to technology readiness level (TRL) approach (developed by NASA), and elaborated knowledge readiness level (KaRL). KaRL is a nine-scale system to categorize data and knowledge (documents) into levels of readiness for particular purposes and to enhance readiness level by using quality and completeness filters, compliance requirements, nanorisk-related tools, stakeholders’ input. By our approach we addressed key issues in nanotechnology such as societal and ethical concerns, circular economy and sustainability, traceability of data, knowledge and decisions.
The main outcomes of the EMPIR project nPSize are presented and the suitability of the new capabilities (e.g. reference materials, measurement procedures, inter-laboratory comparisons) as pertinent contributions to normative projects within CEN/TC 352 Nanotechnologies are discussed. E.g. the first technical report of nPSize on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis (SEM, TSEM, AFM and SAXS) and by machine learning is put at disposal.
A brief perspective of BAM on nanocarriers is presented including examples with special emphasis on the characterization of such materials and underlying challenges. In this respect, also ongoing activities at BAM on different types of core/shell nanomaterials and related systems are briefly summarized.
The use of inorganic lanthanide-doped upconversion nanoparticles (UCNP) in bioimaging and cellular studies requires biocompatible particles. One possible cause of UCNP toxicity is the release of potentially harmful fluoride and lanthanide ions as revealed by dilution studies in aqueous environments, particularly under high dilution conditions. To address this issue, suitable surface coatings preventing such effects in combination with fast screening methods suited for online monitoring and in situ analyses are desired.
Here we present systematic studies of differently sized β-NaYF4:Yb,Er UCNP stabilized with different surface coatings and hydrophilic ligands varying in binding strength to the particle surface in various aqueous environments at different temperatures and UCNP concentrations. The concentration of the fluoride and lanthanide ions released upon particle dissolution was quantified electrochemically with a fluoride ion-sensitive electrode and inductively coupled plasma optical emission spectrometry (ICP-OES) and monitored fluorometrically, thereby exploiting the sensitivity of the upconversion luminescence to changes in size and surface chemistry. Moreover, changes in surface chemistry were determined with X-Ray photoelectron spectroscopy (XPS). Based upon our results, we could derive optimum screening parameters for UCNP stability studies and determine conditions and coating procedures and ligands for enhancing UCNP stability in aqueous environments.
Ionizing radiation damage to DNA plays a fundamental role in cancer therapy. X-ray photoelectron-spectroscopy (XPS) allows simultaneous irradiation and damage monitoring. Although water radiolysis is essential for radiation damage, all previous XPS studies were performed in vacuum. Here we present near-ambient-pressure XPS xperiments to directly measure DNA damage under water atmosphere. They permit in-situ monitoring of the effects of radicals on fully hydrated double-stranded DNA. The results allow us to distinguish direct damage, by photons and secondary low-energy electrons (LEE), from damage by hydroxyl radicals or hydration induced modifications of damage pathways. The exposure of dry DNA to x-rays leads to strand-breaks at the sugar-phosphate backbone, while deoxyribose and nucleobases are less affected. In contrast, a strong increase of DNA damage is observed in water, where OH-radicals are produced. In consequence, base damage and base release become predominant, even though the number of strand-breaks increases further.
The lead authors failed to name two collaborators as co-authors. The authors listed should include:
Miss Claudia L. Compean-Gonzalez (ORCID:
0000-0002-2367-8450) and Dr. Giacomo Ceccone (ORCID:
0000-0003-4637-0771).
These co-authors participated in VAMAS project A27, provided data that were analyzed and presented in this publication (and supporting information), and reviewed the manuscript before submission.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
Low-cost, high-efficient catalysts for water splitting can be potentially fulfilled by developing earthabundant metal oxides. In this work, surface galvanic formation of Co-OH on K0.45MnO2 (KMO) was achieved via the redox reaction of hydrated Co2+ with crystalline Mn4+. The synthesis method takes place at ambient temperature without using any surfactant agent or organic solvent, providing a clean, green route for the design of highly efficient catalysts. The redox reaction resulted in the formation of ultrathin Co-OH nanoflakes with high electrochemical surface area. X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) analysis confirmed the changes in the oxidation state of the bulk and
surface species on the Co-OH nanoflakes supported on the KMO. The effect of the anions, such as chloride, nitrate and sulfate, on the preparation of the catalyst was evaluated by electrochemical and spectrochemical means. XPS and Time of flight secondary ion mass spectrometry (ToF-SIMS) analysis demonstrated that the layer of CoOxHy deposited on the KMO and its electronic structure strongly depend on the anion of the precursor used during the synthesis of the catalyst. In particular, it was found that Cl- favors the formation of Co-OH, changing the rate-determining step of the reaction, which enhances the catalytic activity towards the OER, producing the most active OER catalyst in alkaline media.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
The minimum information requirements needed to guarantee high-quality surface Analysis data of nanomaterials are described with the aim to provide reliable and traceable Information about size, shape, elemental composition and surface chemistry for risk assessment approaches.
The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
The fluorolytic sol–gel synthesis is applied with the intention to obtain two different types of core–shell nanoparticles, namely, SrF2–CaF2 and CaF2–SrF2. In two separate fluorination steps for core and shell formation, the corresponding metal lactates are reacted with anhydrous HF in ethylene glycol. Scanning transmission electron microscopy (STEM) and dynamic light scattering (DLS) confirm the formation of particles with mean dimensions between 6.4 and 11.5 nm. The overall chemical composition of the particles during the different reaction steps is monitored by quantitative Al Kα excitation X-ray photoelectron spectroscopy (XPS). Here, the formation of stoichiometric metal fluorides (MF2) is confirmed, both for the core and the final core–shell particles. Furthermore, an in-depth analysis by synchrotron radiation XPS (SR-XPS) with tunable excitation energy is performed to confirm the core–Shell character of the nanoparticles. Additionally, Ca2p/Sr3d XPS intensity ratio in-Depth profiles are simulated using the software Simulation of Electron Spectra for Surface Analysis (SESSA). In principle, core–shell like particle morphologies are formed but without a sharp interface between calcium and strontium containing phases.
Surprisingly, the in-depth chemical distribution of the two types of nanoparticles is equal within the error of the experiment. Both comprise a SrF2-rich core domain and CaF2-rich shell domain with an intermixing zone between them. Consequently, the internal morphology of the final nanoparticles seems to be independent from the synthesis chronology.
Frass (fine powdery refuse or fragile perforated wood produced by the activity of boring insects) of larvae of the European house borer (EHB) and of drywood termites was tested as a natural and novel feedstock for 3D-printing of wood-based materials. Small particles produced by the drywood termite Incisitermes marginipennis and the EHB Hylotrupes bajulus during feeding in construction timber, were used. Frass is a powdery material of particularly consistent quality that is essentially biologically processed wood mixed with debris of wood and faeces. The filigree-like particles flow easily permitting the build-up of woodbased structures in a layer wise fashion using the Binder Jetting printing process. The Quality of powders produced by different insect species was compared along with the processing steps and properties of the printed parts. Drywood termite frass with a Hausner Ratio HR = 1.1 with ρBulk = 0.67 g/cm3 and ρTap = 0.74 g/cm3 was perfectly suited to deposition of uniformly packed layers in 3D printing. We suggest that a variety of naturally available feedstocks could be used in environmentally responsible approaches to scientific material sciences/additive manufacturing.
Nanomaterials and nanoparticles are finding applications across a wide range of technology sectors, from medicine and food to transportation and construction. In order to assess these new materials for potential risks to health and the environment, they need to be well-characterised. The measurement of constituent nanoparticle size, shape, and size distribution are important factors for the risk evaluation process.
EMPIR project Improved traceability chain of nanoparticle size measurements (17NRM04, nPSize) is working to assess a range of traceable nanoparticle measurement approaches, including Scanning Electron Microscopy (also in Transmission Mode), Atomic Force Microscopy and Small Angle X-ray Scattering, and deliver improved calibration methods to users. For the techniques under investigation, physical models of their response to a range of nanoparticle types are developed. Validated reference materials will also be used for an inter-comparison of measurement systems, with an evaluation of the associated measurement uncertainty. With project contributions to standards development work, manufacturers will be better placed to assess the human and environmental risks posed by nanomaterials across a whole range of products.
Surface-supported metal-organic frameworks HKUST-1 (Hong Kong University of Science and Technology) were used as a model system for a development of a near ambient pressure (NAP) XPS based approach to investigate interaction with atmospheres of water, methanol or pyridine at pressures ranging from 1 to 4 mbar. The films were grown on a gold substrate functionalized with a COOH-terminated self-assembled monolayer using liquidphase epitaxy in a step-by-step fashion. Measurement protocols were developed and optimised for different gases in order to obtain spectra of similar quality in terms of signal intensity, noise and shape. Peak shapes were found to depend on the efficiency of charge compensation. Reference measurements in argon proved to be a useful strategy not only for the evaluation of the Cu(II)-fraction in pristine samples, but also to identify the contributions by the respective gas atmosphere to the C 1s and O 1s photoelectron spectra. Reduced copper was found during the exposition of HKUST-1 to water vapour and pyridine, but this effect was not observed in case of methanol. Additionally, it was established that there are no changes in relative Cu(II) percentage with increasing exposure time. This indicates that saturation was reached already at the lowest time of gas exposure. A detailed elucidation of the mechanism of Cu(II) reduction to Cu(I) in HKUST-1 mediated by water and pyridine is part of ongoing work and not in the scope of the present paper.
Search of new strategies for the inhibition of respiratory viruses is one of the urgent health challenges worldwide, as most of the current therapeutic agents and treatments are inefficient. Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) has caused a pandemic and has taken lives of approximately two Million people to date. Even though various vaccines are currently under development, virus, and especially its spike glycoprotein can mutate, which highlights a Need for a broad-spectrum inhibitor. In this work, inhibition of SARS-CoV-2 by graphene platforms with precise dual sulfate/alkyl functionalities is investigated. A series of graphene derivatives with different lengths of aliphatic chains is synthesized and is investigated for their ability to inhibit SARS-CoV-2 and feline coronavirus.
Graphene derivatives with long alkyl chains (>C9) inhibit coronavirus replication by virtue of disrupting viral envelope. The ability of these graphene platforms to rupture viruses is visualized by atomic force microscopy and cryogenic electron microscopy. A large concentration window (10 to 100-fold) where graphene platforms display strongly antiviral activity against native SARS-CoV-2 without significant toxicity against human cells is found. In this concentration range, the synthesized graphene platforms inhibit the infection of enveloped viruses efficiently, opening new therapeutic and metaphylactic avenues against SARS-CoV-2.
Nanoparticles have gained increasing attention in recent years due to their potential and application in different fields including medicine, cosmetics, chemistry, and their potential to enable advanced materials. To effectively understand and regulate the physico-chemical properties and potential adverse effects of nanoparticles, validated measurement procedures for the various properties of nanoparticles need to be developed. While procedures for measuring nanoparticle size and size Distribution are already established, standardized methods for analysis of their surface chemistry are not yet in place, although the influence of the surface chemistry on nanoparticle properties is undisputed. In particular, storage and preparation of nanoparticles for surface analysis strongly influences the analytical results from various methods, and in order to obtain consistent results, sample preparation must be both optimized and standardized. In this contribution, we present, in detail, some standard procedures for preparing nanoparticles for surface analytics. In principle, nanoparticles can be deposited on a suitable substrate from suspension or as a powder. Silicon (Si) Wafers are commonly used as substrate, however, their cleaning is critical to the process. For sample preparation from suspension, we will discuss drop-casting and spin-coating, where not only the cleanliness of the substrate and purity of the suspension but also its concentration play important roles for the success of the preparation methodology.
For nanoparticles with sensitive ligand shells or coatings, deposition as powders is more suitable, although this method requires particular care in fixing the sample.