Maldonadorosendahl6215

Furthermore, the adsorption efficiency of TiO2@1T/2H-MoS2was unique, 20 ppm solutions of RhB and MB were removed after 1 and 2 min, respectively. The superior adsorption performance of the TiO2@1T/2H-MoS2can be attributed to its high surface area (279.9 m2g-1) and the rich concentration of active sites. The kinetics and the isothermal analysis revealed that the TiO2@1T/2H MoS2heterstructures have maximum adsorption capacity of 1200 and 970 mg g-1for RhB and MB, respectively. This study provides a powerful way for designing an effective photocatalyst and adsorbent TiO2-based nanocomposites for water remediation.We study the suppression of superconductivity with ultrashort laser pulse in the presence of transport current. The theoretical model is based on the Bardeen-Cooper-Schrieffer relations for the superconducting state coupled with kinetic equations for nonequilibrium Bogoliubov quasiparticles and phonons. see more The results of numerical simulation for picosecond and femtosecond laser pulses of optical and infrared ranges are given. We discuss the effects of main problem parameters, including the current density.Li3((LiCr)(Te/Sb))O6compounds where Cr atoms along with Li and Te or Sb are part of a honeycomb and are studied using magnetic susceptibility, specific heat, x-ray photoelectron spectroscopy and neutron diffraction. The oxides stoichiometries as determined from the neutron diffraction studies are Li4.47Cr0.53TeO6and Li3.88Cr1.12SbO6with a stable oxidation state of +3 for Cr. Both the compounds crystallize in space groupC2/mwith intermixing of cations at the 4gsites leaving the 2asites preferentially for Te or Sb. Again, the Li+ions alone predominantly occur in the interlayer sites. Both the compounds show a broad anomaly in specific heat at 8 K, which is robust against 8 T. A corresponding anomaly is absent in the magnetic susceptibility but recovers from its derivative, dχ(T)/dT. We ascertain the magnetic anomaly temperatures (Ta) of Li4.47Cr0.53TeO6and Li3.88Cr1.12SbO6as 5.9 K and 6.7 K respectively from specific heat. Although the physical properties indicated a low temperature anomaly, neutron diffraction data did not reveal a magnetic signal or a structural anomaly down to 1.5 K. This rules out a conventional long-range ordered magnetic ground state in either compounds. Combining the results from specific heat, neutron diffraction and electron paramagnetic resonance, we put forth a scenario of depleted honeycomb lattice of Cr3+with predominant short-range magnetic correlations as the magnetic ground states of the title compounds.The recent interest in the low-energy states in vortices of semiconductor-superconductor heterostructures are mainly fuelled by the prospects of using Majorana zero modes for quantum computation. The knowledge of low-lying states in the vortex core is essential as they pose a limitation on the topological computation with these states. Recently, the low-energy spectra of clean heterostructures, for superconducting-pairing profiles that vary slowly on the scale of the Fermi wavelength of the semiconductor, have been analytically calculated. In this work, we formulate an alternative method based on perturbation theory to obtain concise analytical formulas to predict the low-energy states including explicit magnetic-field and gap profiles. We provide results for both a topological insulator (with a linear spectrum) as well as for a conventional electron gas (with a quadratic spectrum). We discuss the spectra for a wide range of parameters, including both the size of the vortex and the chemical potential of the semiconductor, and thereby provide a tool to guide future experimental efforts. We compare these findings to numerical results.Recent advances in the nanofabrication and modeling of metasurfaces have shown the potential of these systems in providing unprecedented control over light-matter interactions at the nanoscale, enabling immediate and tangible improvement of features and specifications of photonic devices that are becoming always more crucial in enhancing everyday life quality. In this work, we theoretically demonstrate that metasurfaces made of periodic and non-periodic deterministic assemblies of vertically aligned semiconductor nanowires can be engineered to display a tailored effective optical response and provide a suitable route to realize advanced systems with controlled photonic properties particularly interesting for sensing applications. The metasurfaces investigated in this paper correspond to nanowire arrays that can be experimentally realized exploiting nanolithography and bottom-up nanowire growth methods the combination of these techniques allow to finely control the position and the physical properties of each individual nanowire in complex arrays. By resorting to numerical simulations, we address the near- and far-field behavior of a nanowire ensemble and we show that the controlled design and arrangement of the nanowires on the substrate may introduce unprecedented oscillations of light reflectance, yielding a metasurface which displays an electromagnetic behavior with great potential for sensing. Finite-difference time-domain numerical simulations are carried out to tailor the nanostructure parameters and systematically engineer the optical response in the VIS-NIR spectral range. By exploiting our computational-methods we set-up a complete procedure to design and test metasurfaces able to behave as functional sensors. These results are especially encouraging in the perspective of developing arrays of epitaxially grown semiconductor nanowires, where the suggested design can be easily implemented during the nanostructure growth, opening the way to fully engineered nanowire-based optical metamaterials.Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.