Bi0.9Ba0.1Fe0.95O3 and Bi0.9Ba0.1FexTi0.05O3 (x=0.95, 0.925, 0.90) ceramics were prepared through conventional solid state reactions. X-ray diffraction analyses indicated that a high content of perovskite phase was obtained for all the four compositions. While the three (Ba,Ti)-codoped compositions all showed a higher resistivity than Bi0.9Ba0.1Fe0.95O3, and Bi0.9Ba0.1Fe0.925Ti0.05O3 had the best electrical and dielectric properties among the three (Ba,Ti)-codoped compositions, including the largest dielectric constant, the smallest dielectric loss at low frequency range, and the highest electrical resistivity. Magnetic hysteresis loop measurement revealed that the four compositions had similarly enhanced magnetic properties. It is concluded that much attention should be paid to fine composition adjustment when multiple elements are co-doped to BiFeO3 system.

Electrical properties of thin high-k dielectric films are influenced (or even governed) by the presence of macroscopic, microscopic and atomic-size defects. For most applications, a structurally perfect dielectric material with moderate parameters would have sufficiently low leakage and sufficiently long lifetime. But defects open new paths for carrier transport, increasing the currents by orders of magnitude, causing instabilities due to charge trapping, and promoting the formation of defects responsible for electrical breakdown events and for the failure of the film. We discuss how currents flow across the gate stack and how damage is created in the material. We also illustrate the contemporary basic knowledge on hazardous defects (including certain impurities) in high-k dielectrics using the example of a family of materials based on Pr oxides. As an example of the influence of stoichiometry on the electrical pa-rameters of the dielectric, we analyze the effect of nitrogen incorporation into ultrathin Hf silicate films.

The U/n method is a well-established means of improving flux pinning and critical current performance in cuprate superconductors. The method involves the doping of the superconductor with 235 U followed by irradiation with thermal neutrons to promote fission. The resultant columnar damage tracks produced by the energetic fission products pin flux vortices and improve critical current performance in magnetic fields. No such improvement could be observed when the U/n method was applied to MgB 2 superconductor. No fission tracks could be observed in TEM, even for samples that were irradiated at the highest fluence. Gamma-ray spectroscopy indicated that fission had occurred in the expected way.

Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course design and teaching. However, understanding of how learners invest and manage their time during online learning remains limited.Objectives: This study aims to discover the typical time-investment patterns of MOOC learners and their temporal-learning characteristics based on a systematic time-investment analysis framework and their relationship with learning performance.Methods: Based on a proposed time-investment-analysis framework, this study applied statistical, cluster and lag sequential analyses to investigate learners' timeinvestment patterns and their relationships with learning performance, session time allocation, and learning sequences by analysing the learning data from 12,463 participants of a Massive Open Online Course (MOOC) in China. Results and Conclusions:Seven time-investment patterns of MOOC learners were defined, and learning performance was found to differ among them. Further analysis shows that high performers invested time throughout the whole course and allocated time to multiple activities, exam-takers performed better in time management and produced more behavioural sequences related to cognitive strategy and recourse use, and learners' motivation and prior knowledge affected the management and effectiveness of their time investment. Implications:The results support the recognition and evaluation of online learning time-investment patterns and suggest relevant cues for improving MOOC design and teaching.