6.1 Oberflächen- und Dünnschichtanalyse
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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.
Lead(II) is a toxic pollutant often found in metalcontaminated soils and wastewaters. In acidic aqueous environments, Pb(II) is highly mobile. Chemical treatment strategies of such systems therefore often include neutralization agents and metal sorbents. Since metal solubility and the retention potential of sorbents depend on the redox state of the aqueous system, we tested the efficiency of the naturally occurring redox-sensitive ferrous iron carbonate mineral siderite to remove Pb(II) from acidic aqueous solutions in batch experiments under oxic and anoxic conditions over a total of 1008 h. Siderite dissolution led to an increase in reactive solution pH from 3 to 5.3 and 6.9, while 90 and 100% of the initial aqueous Pb(II) (0.48 × 10−3 mol kg−1) were removed from the oxic and anoxic systems, respectively.
Scanning and transmission electron microscopy, combined with X-ray absorption and photoelectron spectroscopy, indicated that under oxic conditions, Pb(II) was consumed by cerussite precipitation and inner-sphere surface complexation to secondary goethite.
Under anoxic conditions, Pb(II) was removed by the rapid precipitation of cerussite. This efficient siderite dissolution-coupled sequestration of Pb(II) into more stable solid phases demonstrates this potential method for contaminated water Treatment regardless of the redox environment.
Alkali–silica reaction (ASR) is an ongoing problem that causes damage to concrete constructions and reduces their durability. Therefore, minimizing this undesired reaction is of great interest for both safety and economic reasons. Additives containing high aluminium content are very effective in reducing the release of silica and enhancing the durability of concrete; however, the mechanism for this effect is still under discussion. In this study, an enrichment of aluminium in the near‐surface region was observed for natural quartzite rock after storage in Al (OH)3 and metakaolin as aluminium sources, from which we conclude that the formation of aluminosilicate sheets of a few nanometres inhibits the silica release; this hypothesis is supported by high‐resolution spectra of Al 2p, Si 2p and O 1s.
The image designed by Natalia Cano Murillo and colleagues shows the cross section of a ternary composite (boehmite/polycarbonate/epoxy, 80μm x 80μm). The surface was measured by AFM kelvin probe microscopy, yielding the surface potential which is shown as 3D‐surface and contour lines. The sample was further subjected to AFM force spectroscopy with a lateral resolution of 1μm², yielding the local Young's modulus, projected in false colors on the 3D surface. The ternary system, containing boehmite nanoparticles, shows a broad distribution of modulus, desirable for optimized macroscopic mechanical properties, such as high stiffness as well as toughness.
Controlling thickness and tightness of surface passivation shells is crucial for many applications of core–shell nanoparticles (NP). Usually, to determine shell thickness, core and core/shell particle are measured individually requiring the availability of both nanoobjects. This is often not fulfilled for functional nanomaterials such as many photoluminescent semiconductor quantum dots (QD) used for bioimaging, solid state lighting, and display technologies as the core does not show the applicationrelevant functionality like a high photoluminescence (PL) quantum yield, calling for a whole nanoobject approach. By combining high-resolution transmission electron microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS), a novel whole nanoobject approach is developed representatively for an ultrabright oleic acid-stabilized, thick shell CdSe/CdS QD with a PL quantum yield close to unity. The size of this spectroscopically assessed QD, is in the range of the information depth of usual laboratory XPS. Information on particle size and monodispersity were validated with dynamic light scattering (DLS) and small angle X-ray scattering (SAXS) and compared to data derived from optical measurements. In addition to demonstrating the potential of this novel whole nanoobject approach for determining architectures of small nanoparticles, the presented results also highlight challenges faced by different sizing and structural analysis methods and method-inherent uncertainties.
Based on practical examples of analysis with Electron Microscopy and Energy-Dispersive X-ray Spectroscopy, the minimum information requirements for reliable and reproducible nanomaterial characterization data such as particle size and shape distribution and elemental analysis are presented and discussed.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
In this paper one refined approach is applied to determine the exact orientation of bipyramidal TiO2 nanoparticles prepared with good dispersion as almost isolated particles on a carbon TEM grid. The advantages of the recently developed high-throughput Transmission Kikuchi Diffraction (TKD) are used to identify quickly and reliably the geometrical orientation of the crystalline TiO2 nanoparticle bipyramids (anatase) on a statistically relevant number of particles.
The present study addresses the capability of measurement of relative particle number concentration by scanning electron microscopy for model bimodal silica and gold samples prepared in the frame of the European research project “nPSize - Improved traceability chain of nanoparticle size measurements” as candidate reference nanoparticles.
Background, Motivation and Objective
Nanomaterials are at the core of some of the 21st century’s most promising technologies. In order to utilize and rationally design materials at the nanoscale the reliable characterization of their physico-chemical properties is highly important. Furthermore, the European Commission has taken measures via the REACH Regulations to control the classification of nanomaterials. REACH Annexes which entered into force in January 2020 require manufacturers to register nanomaterials that are traded in larger quantities (at least 1 ton). Every powder or dispersion where 50% (number distribution) of the constituent particles have sizes ≤ 100 nm in at least one dimension are defined as a nanomaterial. This creates a need for both industrial manufacturers and research and analytical service facilities to reliably characterize potential nanomaterials. Currently, BAM is working on developing reference nanoparticles, which shall expand the scarce list of worldwide available nano reference materials certified for particle size distribution, but also targeting other key parameters such as shape, structure (including porosity) and functional properties. Thus, candidate materials are considered to complement the already available spherical and monodisperse silica, Au and polystyrene reference nanoparticles, e.g. iron oxide and titanium oxide, with an average atomic number between those of silica and gold. Particularly for the imaging by electron microscopies, new nanoparticles of well-defined size in the range of 10 nm are decisive for the accurate particle segmentation by setting precise thresholds.
Statement of Contribution/Methods
Synthesis: Highly monodisperse iron oxide nanoparticles can be synthesized in large quantities by thermal decomposition of iron oleate or iron acetylacetonate precursors in high boiling solvents such as octadecene or dioctyl ether in the presence of oleic acid and oleylamine as capping agents.
Scanning Electron Microscope: An SEM of type Supra 40 from Zeiss has been used including the dedicated measurement mode transmission in SEM (STEM-in-SEM) with a superior material contrast for the nanoparticle analysis. The software package ImageJ has been used for the analysis of the STEM-in-SEM images and to determine the particle size distribution.
Dynamic Light scattering (DLS): Particles in suspension were measured in comparison by means of Zetasizer Nano (Malvern Panalytical; cumulants analysis) and NanoFlex (Microtrac; frequency power spectrum).
Results/Discussion
In this study iron oxide nanoparticles synthesized at BAM and pre-characterized by DLS, SEM (including the transmission mode STEM-in-SEM) are presented. The particles are spherical and highly monodisperse with sizes slightly larger than 10 nm.
In this work, we present various promising sample preparation procedures aiming to avoid drying artifacts and guarantee homogeneously distributed NP samples for imaging techniques like TEM, SEM or AFM. This involves retarded drying in an environment of high relative humidity as well as spin coating or freeze drying of the sample on the substrate.
Depending on the type of the NPs, different techniques are successful in obtaining homogenous sample deposition. Simple drying at ambient conditions often results in heavily agglomerated NPs densely packed in coffee ring traces. Retardation of the drying speed in an environment of high relative humidity, however, yields homogeneous NP deposition in the case of bimodal SiO2 and bimodal gold NP’s. In contrast, a sample of bipyramidal TiO2, heavily agglomerated when commonly dried, is successfully dispersed by spin coating the suspension on the substrate. The analysis of the particle size distribution of the bimodal silica and gold samples yielded number ratios resembling the nominal number ratios. The reliable measurement of the NP concentration of polydisperse samples by electron microscopy, even if relative, is new and of special relevance because up to now only the size of the particles, i.e. the abscissa of the PSD, can be measured reliably.