Stabilization of 6H-Hexagonal SrMnO3 Polymorph by Al2O3 insertion

We demonstrate the structural evolution of polymorphic phases in Al₂O₃-inserted SrMnO₃ ceramics synthesized by solid state reaction. While the 4H-hexagonal phase is predominant in pure SrMnO₃ ceramics, a small amount of 6H-hexagonal polymorph is identified in addition to the primary 4H-hexagonal SrMnO₃ and the secondary hexagonal SrAl₂O₄ phases in the as-sintered ceramics, evidenced by x-ray diffraction and subsequent Rietveld refinement analyses. The existence of the 6H-hexagonal SrMnO₃ phase is corroborated using Raman spectroscopy. The chemical compositions and electronic structures of the Al₂O₃-inserted SrMnO₃ compounds are also examined using energy dispersive spectroscopy and x-ray photoelectron spectroscopy, respectively. The first-principles calculations reveal that there is no clear difference between the total energies of 4H- and 6H-hexagonal polymorphs regardless of the presence/absence of Sr and oxygen vacancies. Possible origins are discussed with the estimation of actual strain based on the refined lattice parameter of 6H SrMnO₃.     

Fabrication of core–shell nanocomposites with enhanced photocatalytic efficacy

The current study establishes the unprecedented involvement in the evolution and production of novel core–shell nanocomposites composed of nanosized titanium dioxide and aniline‐o‐phenylenediamine copolymer. TiO₂@copoly(aniline and o‐phenylenediamine) (TiO₂@PANI‐o‐PDA) core–shell nanocomposites were chemically synthesized in a molar ratio of 5:1 of the particular monomers and several weights of nano‐TiO₂ via oxidative copolymerization. The construction of the TiO₂@PANI‐o‐PDA core–shell nanocomposites was ascertained from Fourier transform IR spectroscopy, UV–visible spectroscopy and XRD. A reasonable thermal behavior for the original copolymer and the TiO₂@PANI‐o‐PDA core–shell nanocomposites was investigated. The bare PANI‐o‐PDA copolymer was thermally less stable than the TiO₂@PANI‐o‐PDA nanocomposites. The core–shell feature of the nanocomposites was found to have core and shell sizes of 17 nm and 19–26 nm, respectively. In addition, it was found that the addition of a high ratio of TiO₂ nanoparticles increases the electrical conductivity and consequently lowers the electrical resistivity of the TiO₂@PANI‐o‐PDA core–shell nanocomposites. The hybrid photocatalysts exhibit a dramatic photocatalytic efficacy of methylene blue degradation under solar light irradiation. A plausible interpretation of the photocatalytic degradation results of methylene blue is also demonstrated. Our setup introduces a facile, inexpensive, unique and efficient technique for developing new core–shell nanomaterials with various required functionalities and colloidal stabilities. © 2018 Society of Chemical Industry     

Alumina-supported catalysts for propane oxidative dehydrogenation from mixed VXM(6−X)O19 (M=W, Mo) hexametalate precursors

γ-Al₂O₃ supported vanadium oxides were modified by tungsten and molybdenum oxides in order to improve dispersion and selectivity towards olefins in propane oxidative dehydrogenation (ODH). Both vanadium–tungsten and vanadium–molybdenum catalysts were obtained by adsorption of mixed isopolyanions (VW₅O₁₉⁵⁻, V₂W₄O₁₉⁴⁻, VMo₅O₁₉⁵⁻ and V₂Mo₄O₁₉⁴⁻) from aqueous solutions. The isopolyanion solutions were characterized by UV-Vis and ⁵¹V NMR spectroscopy. Vanadium, vanadium–tungsten and vanadium–molybdenum precursors and catalysts were also characterized by UV-Vis (diffuse reflectance) and solid state ⁵¹V NMR spectroscopy. An improved selectivity to propene in the presence of tungsten and molybdenum in VO_x/γ-Al₂O₃ was observed and attributed to dilution of vanadium by tungsten or molybdenum oxides on the γ-Al₂O₃ surface.     

Effect of alendronate treatment on the osteoclastogenic potential of bone marrow cells in mice

The expression of cytokeratins (CK), involucrin, vimentin, CD34, and alpha-smooth-muscle actin was studied in fetal and adult hair follicles. The first stage of the developing hair follicle is characterized by palisaded, elongated epithelial cells budding from the epidermal basal layer. These cells express CK5/6, CK14, CK17, CK19, and vimentin. During the following weeks of gestation, different structures in the developing hair follicle can be identified and characterized. The matrical cells display only CK19. The keratinocytes of the outer root sheath express CK5/6, CK14, CK17, CK19, and involucrin; those of the inner root sheath, CK4, CK18, and involucrin; those of the isthmus, the same profile as the ORS. In the infundibulum, the basal-layer keratinocytes express CK5/6, CK14, CK17, and CK19, whereas in the suprabasal layers CK1, CK4, CK10, CK14, and CK17 are seen. The adult hair follicle in anagen fails to express CK19 in the matrical cells and isthmus and both CK17 and CK19 in the infundibulum. These profiles of intermediate filaments and other markers appear to be potentially useful in categorizing neoplasms with apparent follicular differentiation.     

Selection of network and learning parameters for an adaptive neural robotic control scheme

This paper presents the results of a study in the design of a neural network based adaptive robotic control scheme. The neural network used here is a two hidden layer feedforward network and the learning scheme is the well-known backpropagation algorithm. The neural network essentially provides the inverse of the plant and acts in conjunction with a standard PD controller in the feedback loop. The objective of the controller is to accurately control the end position of a single link manipulator in the presence of large payload variations, variations in the link length and also variations in the damping constant. Based on results of this study, guidelines are presented in selecting the number of neurons in the hidden layers and also the parameters for the learning scheme used for training the network. Results also indicate that increasing the number of neurons in the hidden layer will improve the convergence speed of learning scheme up to a certain limit beyond which the addition of neurons will cause oscillations and instability. Guidelines for selecting the proper learning rate, momentum and fast backpropagation constant that ensure stability and convergence are presented. Also, a relationship between the r.m.s. error and the number of iterations used in training the neural network is established.