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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.
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.
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.