240 research outputs found
Photoconductivity of Single-crystalline Selenium Nanotubes
Photoconductivity of single-crystalline selenium nanotubes (SCSNT) under a
range of illumination intensities of a 633nm laser is carried out with a novel
two terminal device arrangement at room temperature. It's found that SCSNT
forms Schottky barriers with the W and Au contacts, and the barrier height is a
function of the light intensities. In low illumination regime below 1.46x10E-4
muWmum-2, the Au-Se-W hybrid structure exhibits sharp switch on/off behavior,
and the turn-on voltages decrease with increasing illuminating intensities. In
the high illumination regime above 7x10E-4 muWmum-2, the device exhibits ohmic
conductance with a photoconductivity as high as 0.59Ohmcm-1, significantly
higher that reported values for carbon and GaN nanotubes. This finding suggests
that SCSNT is potentially a good photo-sensor material as well we a very
effective solar cell material.Comment: 12pages including 5 figures, submitted to Nanotechnolog
On-the-fly machine learning for parametrization of the effective Hamiltonian
The first-principles-based effective Hamiltonian is widely used to predict
and simulate the properties of ferroelectrics and relaxor ferroelectrics.
However, the parametrization method of the effective Hamiltonian is complicated
and hardly can resolve the systems with complex interactions and/or complex
components. Here, we developed an on-the-fly machine learning approach to
parametrize the effective Hamiltonian based on Bayesian linear regression. The
parametrization is completed in molecular dynamics simulations, with the
energy, forces and stress predicted at each step along with their
uncertainties. First-principles calculations are executed when the
uncertainties are large to retrain the parameters. This approach provides a
universal and automatic way to compute the effective Hamiltonian parameters for
any considered systems including complex systems which previous methods can not
handle. BaTiO3 and Pb(Sc,Ta)O3 are taken as examples to show the accurateness
of this approach comparing with conventional first-principles parametrization
method.Comment: 11 pages, 2 figure
Negative electrocaloric effect in nonpolar phases of perovskite over wide range of temperature
The electrocaloric effect (ECE) offers a promising alternative to the
traditional gas compressing refrigeration due to its high efficiency and
environmental friendliness. The unusual negative electrocaloric effect refers
to the adiabatic temperature drops due to application of electric field, in
contrast with the normal (positive) ECE, and provides ways to improve the
electrocaloric efficiency in refrigeration cycles. However, negative ECE is
unusual and requires a clear understanding of microscopic mechanisms. Here, we
found unexpected and extensive negative ECE in nonpolar orthorhombic,
tetragonal, and cubic phases of halide and oxide perovskite at wide range of
temperature by means of first-principle-based large scale Monte Carlo methods.
Such unexpected negative ECE originates from the octahedral tilting related
entropy change rather than the polarization entropy change under the
application of electric field. Furthermore, a giant negative ECE with
temperature change of 8.6 K is found at room temperature. This giant and
extensive negative ECE in perovskite opens up new horizon in the research of
caloric effects and broadens the electrocaloric refrigeration ways with high
efficiency.Comment: 11 pages, 7 figure
Task-aware Adaptive Learning for Cross-domain Few-shot Learning
Although existing few-shot learning works yield promising results for in-domain queries, they still suffer from weak cross-domain generalization. Limited support data requires effective knowledge transfer, but domain-shift makes this harder. Towards this emerging challenge, researchers improved adaptation by introducing task-specific parameters, which are directly optimized and estimated for each task. However, adding a fixed number of additional parameters fails to consider the diverse domain shifts between target tasks and the source domain, limiting efficacy. In this paper, we first observe the dependence of task-specific parameter configuration on the target task. Abundant task-specific parameters may over-fit, and insufficient task-specific parameters may result in under-adaptation -- but the optimal task-specific configuration varies for different test tasks. Based on these findings, we propose the Task-aware Adaptive Network (TA2-Net), which is trained by reinforcement learning to adaptively estimate the optimal task-specific parameter configuration for each test task. It learns, for example, that tasks with significant domain shift usually have a larger need for task-specific parameters for adaptation. We evaluate our model on Meta-dataset. Empirical results show that our model outperforms existing state-of-the-art methods
Zeeman effect in centrosymmetric antiferromagnets controlled by an electric field
Centrosymmetric antiferromagnetic semiconductors, although abundant in
nature, seem less promising than ferromagnets and ferroelectrics for practical
applications in semiconductor spintronics. As a matter of fact, the lack of
spontaneous polarization and magnetization hinders the efficient utilization of
electronic spin in these materials. Here, we propose a paradigm to harness
electronic spin in centrosymmetric antiferromagnets via Zeeman spin splittings
of electronic energy levels -- termed as spin Zeeman effect -- which is
controlled by electric field.By symmetry analysis, we identify twenty-one
centrosymmetric antiferromagnetic point groups that accommodate such a spin
Zeeman effect. We further predict by first-principles that two
antiferromagnetic semiconductors, FeTeO and SrFeSO, are
excellent candidates showcasing Zeeman splittings as large as 55 and
30 meV, respectively, induced by an electric field of 6 MV/cm. Moreover,
the electronic spin magnetization associated to the splitting energy levels can
be switched by reversing the electric field. Our work thus sheds light on the
electric-field control of electronic spin in antiferromagnets, which broadens
the scope of application of centrosymmetric antiferromagnetic semiconductors
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