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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.
Die gemeinsame Forschungsstrategie der Bundesoberbehörden zur Nanotechnologie wurde 2016 veröffentlicht. Die darin enthaltenen Aufgaben wurden von den Bundesoberbehörden vielfältig bearbeitet. Diese Präsentation gibt einen Überblick über die Projekte, die von der BAM bis 2019 bearbeitet wurden/werden und sich in den Rahmen der Forschungsstrategie einordnen.
Organic and inorganic micro- and nanoparticles are increasingly used as drug carriers, fluorescent sensors, and multimodal labels in the life and material sciences. Typically, these applications require further functionalization of the particles with, e.g., antifouling ligands, targeting bioligands, stimuli-responjsive caps, or sensor molecules. Besides serving as an anchor point for subsequent functionalization, the surface chemistry of these particles also fundamentally influences their interaction with the surrounding medium and can have a significant effect on colloidal stability, particle uptake, biodistribution, and particle toxicity in biological systems. Moreover, functional groups enable size control and tuning of the surface during the synthesis of particle systems.
For these reasons, a precise knowledge of the chemical nature, the total number of surface groups, and the number of groups on the particle surface that are accessible for further functionalization is highly important. In this contribution, we will will discuss the advantages and limitiations of different approaches to quantify the amount of commonly used surface functional groups such as amino,[1,2] carboxy,[1,2] and aldehyde groups.[3] Preferably, the quantification is carried out using sensitive and fast photometric or fluorometric assays, which can be read out with simple, inexpensive instrumentation and can be validated by complimentary analytic techniques such as ICP-OES and quantitative NMR.
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.
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.
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.