Rational Design of Two-Dimensional Magnetic Chromium Borides Based on First-Principles Calculation

Two-dimensional(2D) magnetic materials have been experimentally recognized recently,however,the Curie temperatures(TC) of known 2D systems are quite low.Generally,magnetic systems can be seen as constituent magnetic elements providing spins and the non-magnetic elements providing frameworks to host the magnetic elements.Short bond lengths between the magnetic and non-magnetic elements would be beneficial for strong magnetic interactions and thus high TC.Based on this,we propose to combine the magnetic element Cr and the non-magnetic element boron to design novel 2D magnetic systems.Using our self-developed software package IM2 ODE,we design a series of chromium-boride based 2D magnetic materials.Nine stable magnetic systems are identified.Among them,we find that CrB4-Ⅰ,CrB4-Ⅱ and CrBs-Ⅰ with common structural units [CrB8] are ferromagnetic metals with estimated TC of 270 K,120 K and 110 K,respectively.On the other hand,five CrB3 phases with structural units [Cr2B12] are antiferromagnetic metals.Additionally,we also find one antiferromagnetic semiconductor CrB2-Ⅰ.Our work may open new directions for identifying 2D magnetic systems with high TC.     

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.     

单载流子传输的双异质结光敏晶体管探测器的研究

基于器件仿真器Atlas, 建立了InP/InGaAsP单向载流子传输的双异质结光敏晶体管(UTC-DHPT)的二维模型, 分析讨论了器件性能与外延结构参数的关系. 设计出同时具有高响应度(≥17.93 A/W)和高特征频率(≥121.68 GHz)的UTC-DHPT, 缓解了传统的异质结光敏晶体管光电探测器中探测效率和工作速度的矛盾. 关键词： 单向载流子 光敏晶体管 电流放大倍数 响应度     

Adsorption of a spherical nanoparticle in polymer brushes: Brownian dynamics investigation

We use Brownian dynamics simulations to study the adsorption behavior of a nanosized particle in polymer brushes. The adsorption process, the dynamic behavior of the nanoparticle in the brush, the penetration depth, the diffusion coefficient of the nanoparticle in different depths of the brush, and the forces exerted on the nanoparticle by the surrounding brush are all investigated for different grafting densities.     

High resolution dynamic cardiac MRI using partial separability of spatiotemporal signals with a novel sampling scheme

The partial separability (PS) of spatiotemporal signals has been exploited to accelerate dynamic cardiac MRI by sampling two datasets (training and imaging datasets) without breath-holding or ECG triggering. According to the theory of partially separable functions, the wider the range of spatial frequency components covered by the training dataset, the more accurate the temporal constraint imposed by the PS model. Therefore, it is necessary to develop a new sampling scheme for the PS model in order to cover a wider range of spatial frequency components. In this paper, we propose the use of radial sampling trajectories for collecting the training dataset and Cartesian sampling trajectories for collecting the imaging dataset. In vivo high resolution cardiac MRI experiments demonstrate that the proposed data sampling scheme can significantly improve the image quality. The image quality using the PS model with the proposed sampling scheme is comparable to that of a commercial method using retrospective cardiac gating and breath-holding. The success of this study demonstrates great potential for high-quality, high resolution dynamic cardiac MRI without ECG gating or breath-holding through use of the PS model and the novel data sampling scheme.     

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A three-channel far-infrared (FIR) HCN laser interferometer has been developed to measure the line averaged electron density on EAST tokamak. Detectors with the properties of high sensitivity, high signal-to-noise ratio, small volume and stability are needed in interferometer device. The application of new Schottky barrier diode (SBD) detector has made the density signal and the signal-to-noise ratio greatly improved. The disturbance caused by the lower hybrid wave can be eliminated by using double copper foil shield and narrow band pass filter. The improved detectors have worked well during the long pulse discharge and H mode discharge.     

Selection of optimal combinations of inputs in a partial least squares model for prediction of soil organic matter

Abstract

Partial least squares model is widely used in estimation of soil physical and chemical parameters such as soil organic matter and moisture content, due to its advantages in dealing with collinearity of variables like hyperspectral reflectance. However, it is hard to determine optimal combination of partial least squares model input for soil organic matter prediction since there are lots of possibilities such as, different mathematical transformation of spectral reflectance, wavelength ranges, and spectral resolution. Laboratory hyperspectral reflectance of soils in Songnen plain were analyzed in this study, and the orthogonal experimental design method for deriving optimal combination of input variables for soil organic matter prediction models was introduced. For intercalating orthogonal experimental design table, five different levels which commonly used by researchers were assigned to factors. Results show that the optimal combination input for single black soil is using the derivative logarithmic reciprocal reflectance in the wavelength range selected by multiple stepwise regression at a spectral resolution of 5?nm (R²=?0.95, RMSE?=?0.21, and RPD?=?4.49), and different soils is using continuum removed in the wavelength range selected by MSR at a spectral resolution of 5?nm (R²?=?0.77, RMSE?=?0.74, and RPD?=?2.08). With optimal combination input, the partial least squares model prediction ability was evaluated as excellent for single black soil, possible for different soils. This study illustrates the orthogonal experimental design method can be an effective way to identify the optimal input variables of a partial least squares model for soil organic matter prediction, and multiple stepwise regression can be a preprocessing step to reduce hyperspectral data redundancy before using partial least squares to predict soil organic matter. Overall, this study provides a new approach for determining optimal input of partial least squares predicting model.     

ZnO nanowire array ultraviolet photodetectors with self-powered properties

A ZnO nanowire (NW) array ultraviolet photodetector (PD) with Pt Schottky contacts has been fabricated on a glass substrate. Under UV light illumination, this PD showed a high photo-to-dark current ratio of 892 at 30 V bias. Interestingly, it was also found that this PD had a high sensitivity of 475 without external bias. This phenomenon could be explained by the asymmetric Schottky barrier height (SBH) at the two ends causing different separation efficiency of photogenerated electron–hole pairs, which resulted in the formation of photocurrent. It is anticipated to have potential applications in self-powered UV detection field.     

A Novel Approach to All-Optical Generation of Ultra-Wideband Signals

Abstract

The generation of ultra-wideband signals in the optical domain is highly desirable for ultra-wideband-over-fiber systems, which has recently become a topic of interest. In this article, a novel and simple approach to achieve all-optical generation of ultra-wideband signals is proposed, which is based on delaying and superimposing optical Gaussian pulses with opposite polarities. The proposed system is capable of generating both ultra-wideband monocycle and doublet pulses, and the polarity of the generated ultra-wideband monocycle pulses can be fast-switched to implement pulse polarity modulation with the required bit pattern. A model to describe the proposed system is developed, and the generation of ultra-wideband signals is demonstrated with simulations and a proof-of-concept experiment.     

生物柴油的制备和应用研究进展

生物柴油作为一种绿色可再生、生物可降解和无毒的替代燃料,引起了人们极大的关注.本文综述了国内外生物柴油的研究、生产和应用现状,介绍了生物柴油的主要特性,详述了直接混合、微乳液、高温热裂解、化学酯交换等生产生物柴油的方法.阐述了我国发展生物柴油的重要意义以及新疆地区发展生物柴油的优势.