Preferred oriented ZnFe2O4 nanowire arrays with an average diameter of 16 nm were fabricated by post-annealing of ZnFe2 nanowires within anodic aluminum oxide templates in atmosphere. Selected area electron diffraction and X-ray diffraction exhibit that the nanowires are in cubic spinel-type structure with a [110] preferred crystallite orientation. Magnetic measurement indicates that the as-prepared ZnFe2O4 nanowire arrays reveal uniaxial magnetic anisotropy, and the easy magnetization direction is parallel to the axis of nanowire. The optical properties show the ZnFe2O4 nanowire arrays give out 370-520 nm blue-violet light, and their UV absorption edge is around 700 nm. The estimated values of direct and indirect band gaps for the nanowires are 2.23 and 1.73 eV, respectively.
The unexpected room temperature ferromagnetism in pure sodium chloride (NaCl) particles with different crystal size synthesized by breaking at different times is attributed to surface defects, which provides a novel opportunity to further understand the origin of ferromagnetism in the traditional "nonmagnetic" inorganic non-metallic materials. The results of X-ray diffraction, scanning electron microscopy, and transmission electron microscopy suggest that breaking progress does not change the samples' body, but drastically reduces the size of the samples, what's more, it is found to enhance the strength of the ferromagnetic component with decreasing the samples' size through magnetism measure; the first-principle calculation results confirm the experimental conclusion. Ferromagnetism originates from surface effect, probably the long range ferromagnetic interactions between the surface Cl vacancies.
During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes.
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