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Reliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.
Angesichts der zunehmenden Digitalisierung und dem Einsatz datenintensiver Methodiken in der Wissenschaft stehen Forschende vor der Herausforderung, stetig wachsende Datenmengen nachvollziehbar zu dokumentieren, langfristig zu speichern und für Dritte nachnutzbar zu machen. Um diesen Anforderungen gerecht zu werden, bietet sich die Nutzung von Software-Lösungen an, welche Forschungsdatenmanagement mit der digitalen Dokumentation von Laborinventar und Experimenten in elektronischen Laborbüchern (engl. electronic lab notebooks (ELN)) verknüpfen.
Quaternary ammonium compounds (QACs) are widely used as active agents in disinfectants, antiseptics, and preservatives. Despite being in use since the 1940s, there remain multiple open questions regarding their detailed mode-of-action and the mechanisms, including phenotypic heterogeneity, that can make bacteria less susceptible to QACs. To facilitate studies on resistance mechanisms towards QACs, we synthesized a fluorescent quaternary ammonium compound, namely N-dodecyl-N,N-dimethyl-[2-[(4-nitro-2,1,3-benzoxadiazol-7-yl)amino]ethyl]azanium-iodide (NBD-DDA). NBD-DDA is readily detected by flow cytometry and fluorescence microscopy with standard GFP/FITC-settings, making it suitable for molecular and single-cell studies. As a proof-of-concept, NBD-DDA was then used to investigate resistance mechanisms which can be heterogeneous among individual bacterial cells. Our results reveal that the antimicrobial activity of NBD-DDA against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa is comparable to that of benzalkonium chloride (BAC), a widely used QAC, and benzyl-dimethyl-dodecylammonium chloride (BAC12), a mono-constituent BAC with alkyl-chain length of 12 and high structural similarity to NBD-DDA. Characteristic time-kill kinetics and increased tolerance of a BAC tolerant E. coli strain against NBD-DDA suggest that the mode of action of NBD-DDA is similar to that of BAC. As revealed by confocal laser scanning microscopy (CLSM), NBD-DDA is preferentially localized to the cell envelope of E. coli, which is a primary target of BAC and other QACs. Leveraging these findings and NBD-DDA‘s fluorescent properties, we show that reduced cellular accumulation is responsible for the evolved BAC tolerance in the BAC tolerant E. coli strain and that NBD-DDA is subject to efflux mediated by TolC. Overall, NBD-DDA’s antimicrobial activity, its fluorescent properties, and its ease of detection render it a powerful tool to study resistance mechanisms of QACs in bacteria and highlight its potential to gain detailed insights into its mode-of-action.
Stability, dissolution, and cytotoxicity of NaYF4‑upconversion nanoparticles with different coatings
(2022)
Upconversion nanoparticles (UCNPs) have attracted considerable attention owing to their unique photophysical properties. Their utilization in biomedical applications depends on the understanding of their transformations under physiological conditions and their potential toxicity. In this study, NaYF4: Yb,Er UCNPs, widely used for luminescence and photophysical studies, were modified with a set of four different coordinatively bound surface ligands, i.e., citrate, alendronate (AA), ethylendiamine tetra(methylene phosphonate) (EDTMP), and poly(maleic anhydride-alt-1-octadecene) (PMAO), as well as silica coatings with two different thicknesses. Subsequently, the aging-induced release of fluoride ions in water and cell culture media and their cytotoxic profile to human keratinocytes were assessed in parallel to the cytotoxic evaluation of the ligands, sodium fluoride and the lanthanide ions. The cytotoxicity studies of UCNPs with different surface modifications demonstrated the good biocompatibility of EDTMP-UCNPs and PMAO-UCNPs, which is in line with the low amount of fluoride ions released from these samples. An efficient prevention of UCNP dissolution and release of cytotoxic ions, as well as low cytotoxicity was also observed for UCNPs with a sufficiently thick silica shell. Overall, our results provide new insights into the
understanding of the contribution of surface chemistry to the stability, dissolution behavior, and cytotoxicity of UCNPs. Altogether, the results obtained are highly important for future applications of UCNPs in the life sciences and bioimaging studies.
BAM is currently building up a platform of novel nanoRMs relying on iron oxide nanoparticles of different shape, size and surface chemistry. Iron oxide was chosen as a core material because of its relevance to the material and life sciences.
As a first candidate of this series, we present cubic iron oxide nanoparticles with a nominal edge length of 8 nm. These particles were synthesized by thermal decomposition of iron oleate in high boiling organic solvents adapting well-known literature procedures. After dilution to a concentration suitable for electron microscopy (TEM and SEM) as well as for small-angle X-ray scattering (SAXS) measurements, the candidate nanoRM was bottled and assessed for homogeneity and stability by both methods following the guidelines of ISO 17034 and ISO Guide 35.
The particle sizes obtained by both STEM-in-SEM and TEM are in excellent agreement with a minimum Feret of 8.3 nm ± 0.7 nm. The aspect ratio (AR) of the iron oxide cubes were extracted from the images as the ratio of minimum Feret to Feret resulting in an AR of 1.18 for TEM to 1.25 for SEM. Alternatively, a rectangular bounding box was fitted originating from the minimum Feret and the longest distance through the particle in perpendicular direction. This led to AR values of 1.05 for TEM and 1.12 for SEM, respectively. The results confirm the almost ideal cubic shape.
Risk assessment of nanomaterials requires not only standardized toxicity studies but also validated methods for nanomaterial surface characterization with known uncertainties. In this context, a first bilateral interlaboratory comparison on Surface group quantification of nanomaterials is presented that assesses different reporter-free and labeling methods for the quantification of the total and accessible number of amine functionalities on commercially available silica nanoparticles that are widely used in the life sciences. The overall goal of this comparison is the identification of optimum methods as well as achievable measurement uncertainties and the comparability of the results across laboratories. We also examined the robustness and ease of implementation of the applied analytical methods and discussed method-inherent limitations. In summary, this comparison presents a first step toward the eventually required standardization of methods for surface group quantification.
Imaging in the shortwave-infrared region (SWIR, λ = 1000–2500 nm) has the potential to enable deep tissue imaging with high resolution. Critical to the development of these Methods is the identification of low molecular weight, biologically compatible fluorescent probes that emit beyond 1000 nm.
Exchanging the bridging oxygen atom on the xanthene scaffold (C10’ position) with electron withdrawing groups has been shown to lead to significant redshifts in absorbance and emission. Guided by quantum chemistry computational modeling studies, we investigated the installation of a Ketone bridge at the C10’ position. This simple modification extends the absorbance maxima to 860 nm and the emission beyond 1000 nm, albeit with reduced photon output. Overall, These studies demonstrate that broadly applied xanthene dyes can be extended into the SWIR range.
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.
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
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.