Surface phase separation in nanosized charge-ordered manganites

Recent experiments showed that the robust charge-ordering in manganites can be weakened by reducing the grain size down to nanoscale. Weak ferromagnetism was evidenced in both nanoparticles and nanowires of charge-ordered manganites. To explain these observations, a phenomenological model based on surface phase separation is proposed. The relaxation of superexchange interaction on the surface layer allows formation of a ferromagnetic shell, whose thickness increases with decreasing grain size. Possible exchange bias and softening of the ferromagnetic transition in nanosized charge-ordered manganites are predicted.Comment: 4 pages, 3 figure

Physio-chemical and antibacterial characteristics of pressure spun nylon nanofibres embedded with functional silver nanoparticles

© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Date of Acceptance: 05/06/2015A novel and facile approach to prepare hybrid nanoparticle embedded polymer nanofibers using pressurised gyration is presented. Silver nanoparticles and nylon polymer were used in this work. The polymer solution's physical properties, rotating speed and the working pressure had a significant influence on the fibre diameter and the morphology. Fibres in the range of 60–500 nm were spun using 10 wt.%, 15 wt.% and 20 wt.% nylon solutions and these bead-free fibres were processed under 0.2 MPa and 0.3 MPa working pressure and a rotational speed of 36,000 rpm. 1–4 wt.% of Ag was added to these nylon solutions and in the case of wt.% fibres in the range 50–150 nm were prepared using the same conditions of pressurised gyration. Successful incorporation of the Ag nanoparticles in nylon nanofibres was confirmed by using a combination of advanced microscopical techniques and Raman spectrometry was used to study the bonding characteristics of nylon and the Ag nanoparticles. Inductively coupled plasma mass spectroscopy showed a substantial concentration of Ag ions in the nylon fibre matrix which is essential for producing effective antibacterial properties. Antibacterial activity of the Ag-loaded nanofibres shows higher efficacy than nylon nanofibres for Gram-negative Escherichia coli and Pseudomonas aeruginosa microorganisms, and both Ag nanoparticles and the Ag ions were found to be the reason for enhanced cell death in the bacterial solutionPeer reviewe

Superconductivity in heavily boron-doped silicon carbide

The discoveries of superconductivity in heavily boron-doped diamond (C:B) in 2004 and silicon (Si:B) in 2006 renew the interest in the superconducting state of semiconductors. Charge-carrier doping of wide-gap semiconductors leads to a metallic phase from which upon further doping superconductivity can emerge. Recently, we discovered superconductivity in a closely related system: heavily-boron doped silicon carbide (SiC:B). The sample used for that study consists of cubic and hexagonal SiC phase fractions and hence this lead to the question which of them participates in the superconductivity. Here we focus on a sample which mainly consists of hexagonal SiC without any indication for the cubic modification by means of x-ray diffraction, resistivity, and ac susceptibility.Comment: 9 pages, 5 figure

Mixed Information Flow for Cross-domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that mixed information flow network is able to further improve recommendation performance in different domains by modeling mixed information flow.Comment: 26 pages, 6 figures, TKDD journal, 7 co-author