Magnetocaloric effect and magnetic properties of Tb0.9 Sn0.1 MnO3

The magnetocaloric effect in magnetic materials is of great interest nowadays. In this article we present an investigation about the magnetic properties near the magnetic transition in a polycrystalline sample of a manganite Tb0.9 Sn0.1 MnO3. Particularly, we are interested in describing the nature of the magnetic interactions and the magnetocaloric effect in this compound. The temperature dependence of the magnetization was measured to determine the characteristics of the magnetic transition and the magnetic entropy change was calculated from magnetization curves at different temperatures. The magnetic solid is paramagnetic at high temperatures. We observe a dominant antiferromagnetic interaction below Tn =38 K for low applied magnetic fields; the presence of Sn doping in this compound decreases the Ńel temperature of the pure TbMnO3 system. A drastic increase in the magnetization as a function of temperature near the magnetic transition suggests a strong magnetocaloric effect. We found a large magnetic entropy change Δ SM (T) of about -4 J/kg K at H=3 T. We believe that the magnetic entropy change is associated with the magnetic transition and we interpret it as due to the coupling between the magnetic field and the spin ordering. This relatively large value and broad temperature interval (about 35 K) of the magnetocaloric effect make the present compound a promising candidate for magnetic refrigerators at low temperatures. © 2007 American Institute of Physics.

Nanoengineering coaxial carbon nanotube-dual-polymer heterostructures.

We describe studies of new nanostructured materials consisting of carbon nanotubes wrapped in sequential coatings of two different semiconducting polymers, namely, poly(3-hexylthiophene) (P3HT) and poly(9,9'-dioctylfluorene-co-benzothiadiazole) (F8BT). Using absorption spectroscopy and steady-state and ultrafast photoluminescence measurements, we demonstrate the role of the different layer structures in controlling energy levels and charge transfer in both solution and film samples. By varying the simple solution processing steps, we can control the ordering and proportions of the wrapping polymers in the solid state. The resulting novel coaxial structures open up a variety of new applications for nanotube blends and are particularly promising for implementation into organic photovoltaic devices. The carbon nanotube template can also be used to optimize both the electronic properties and morphology of polymer composites in a much more controlled fashion than achieved previously, offering a route to producing a new generation of polymer nanostructures.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.