A variety of multisegmented nanorods (NRs) composed of dense Au and porous Rh and Ru segments with lengths controlled down to ca. 10 nm are synthesized within porous anodic aluminum oxide membranes. Despite the high Rh and Ru porosity (i.e., ∼40%), the porous metal segments are able to efficiently couple with the longitudinal localized surface plasmon resonance (LSPR) of Au NRs. Finite-difference time-domain simulations show that the LSPR wavelength can be precisely tuned by adjusting the Rh and Ru porosity. Additionally, light absorption inside Rh and Ru segments and the surface electric field (E-field) at Rh and Ru can be independently and selectively enhanced by varying the position of the Rh and Ru segment within the Au NR. The ability to selectively control and decouple the generation of high-energy, surface hot electrons and low-energy, bulk hot electrons within photocatalytic metals such as Rh and Ru makes these bimetallic structures great platforms for fundamental studies in plasmonics and hot-electron science.
Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder–decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out.
We report on a method of synthesis of single-walled carbon nanotubes percolated networks on silicon dioxide substrates using monodisperse Co and Ni catalyst. The catalytic nanoparticles were obtained by modified method of reverse micelles of bis-(2-ethylhexyl) sulfosuccinate sodium in isooctane solution that provides the nanoparticle size control in range of 1 to 5 nm. The metallic nanoparticles of Ni and Co were characterized using transmission electron microscopy (TEM) and atomic-force microscopy (AFM). Carbon nanotubes were synthesized by chemical vapor deposition of CH 4 /H 2 composition at 1000 °C on catalysts pre-deposited on silicon dioxide substrate. Before the temperature treatment during the carbon nanotube synthesis, the most of the catalyst material agglomerates, due to the magnetic forces, while during the nanotube growth disintegrates into the separate nanoparticles with narrow diameter distribution. The formed nanotube networks were characterized using AFM, scanning electron microscopy (SEM) and Raman spectroscopy. We find that the nanotubes are mainly single-walled carbon nanotubes with high structural perfection up to 200 µm long with diameters from 1.3 to 1.7 nm consistent with catalyst nanoparticles diameter distribution and independent of its material.
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