Ru_n(n=2~8)金属团簇的结构和能级分布的DFT研究

采用密度泛函理论中的广义梯度近似(DFT/GGA)方法,对Run团簇(n = 2~8)的几何结构与稳定性、束缚能以及能级分布的关系进行了研究,并分析了随着团簇原子数的增加,团簇的几何结构和费米能级的变化,结果表明:Ru簇的几何结构在4个原子以前是平面结构,而从5个原子开始为空间立体的稳定结构,束缚能随金属原子数的增加而增加。能级结构呈明显的分立特征,费米能级随原子个数的增加而增加,但从Ru7开始又有所降低,且团簇的能量间隙逐渐减小,趋近于大块金属的能级特征。     

MEASUREMENTS OF CHAIN INTERPENETRATION OF POLYMER GLASSES

The concept of entanglement provides the basis of our current understanding of the flow behavior of polymer melts, Current techniques developed to investigate the degree of interpenetration of polymer chains only provide indirectly the information of the degree of entanglement in a relatively large scale （several to tens of nanometer）. In this article, we report ^1H-NMR spectroscopy with dipolar filters under fast magic angle spinning for probing chain interpenetration of polymer glasses at the molecular level.     

Theoretical Study of Weakly Bound Dimers between Hydrogen Fluoride and Some Polar Molecules

Weakly bound linear and bent dimers, FH—X (where X = CO, OC, CNH, NCH, N₂O and ON₂), are investigated using the DFT B3LYP and ab initio MP2 methods with the same basis sets (6–311++G(3df,2pd)). The strengths of the H—C or H—N H‐bonds in dimers FH—CO, FH—CNH, and FH—N₂O are compared with those of the H—O or H—N H‐bonds in dimers FH—OC, FH—NCH, and FH—ON₂. The results obtained for the H‐bond distances, the elongation effect of the HF bond, the red shift of the HF stretching frequency, and the energy difference between the dimer and the charge transfer reveal that the H‐bonds of the first group of dimers are stronger than those of the second. The Gibbs energies calculated for the six dimer formations indicate that the weakly bound dimers are unstable at room temperature (T = 298 K) (FH—X's → FH + X's, ΔG < 0).     

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

方势阱中束缚态粒子能级的数值方法和波函数的图示

用数值方法求出了一维有限深不对称方势阱中束缚态粒子的能级和归一化波函数及其图示,所得结果在势阱深度趋于无穷大时与无限深势阱的结果一致.     

ZnO薄膜的椭偏和DLTS特性

用射频磁控溅射在硅衬底上淀积氧化锌薄膜,并对样品分别作氮气、空气、氧气等不同条件下退火处理。为研究退火气氛对ZnO/Si薄膜中缺陷以及折射率的影响,由深能级瞬态谱(DLTS)以及椭偏测量方法进行了检测。椭偏测量结果表明相对原始生长的样品,在氮气和空气退火使ZnO薄膜折射率下降,但氧气中退火使折射率升高。我们对折射率的这种变化机理进行了解释。DLTS测量得到一个与Zni**相关的深能级中心E1存在,氧气气氛退火可以消除E1能级。在氮气退火情况下Zn*i*的存在对抑制VO引起的薄膜折射率下降有利。     

利用界面激基复合物发射大致估算稀土配合物LUMO能级

研究了一种利用无荧光稀土配合物与同一受体材料形成的界面激基复合物的发射来大致估算其最低未占据轨道(LUMO)能级简单方法.镥、钆、钪三种稀土配合物与星形爆炸物形成的激基复合物发射峰位分别在590,607和656 nm,与此相对应LUMO能级计算结果大致为3.0,3.06,3.21 eV.     

Effect of Poly(methacrylic acid) on the Cytokine Level in an In Vivo Tumor Model

Cancer is a leading cause of mortality globally. Despite remarkable improvements in cancer-treatment approaches, disease recurrence and progression remain major obstacles to therapy. While chemotherapy is still a first-line treatment for a variety of cancers, the focus has shifted to the development and application of new approaches to therapy. Nevertheless, the relationship between immune response, neoplastic diseases and treatment efficiency is not fully understood. Therefore, the aim of the study was to investigate the immunopharmacological effects of methacrylic acid homopolymer in an in vivo tumor model. Materials and methods: Monomeric methacrylic acid was used to synthesize polymers. Methacrylic acid was polymerized in dioxane in the presence of 4-Cyano-4-[(dodecylsulfanylthiocarbonyl)sulfanyl]pentanoic acid. To study the molecular weight characteristics of PMAA by GPC, carboxyl groups were preliminarily methylated with diazomethane. An experimental cancer model was obtained by grafting RMK1 breast cancer cells. The serum levels of IL-6, IL-10, IL-17, transforming growth factor β1 (TGF-β1), and tumor necrosis factor α (TNF-α) were measured by ELISA. Results: The effect of PMAA on the serum concentrations of several cytokines was studied upon its single administration to laboratory animals in early neoplastic process. The IL-6, IL-17 and TGF-β1 concentrations were found to change significantly and reach the level observed in intact rats. The IL-10 concentration tended to normalize. Conclusion: The positive results obtained are the basis for further studies on the effect of methacrylic-acid polymers with different molecular-weight characteristics on the neoplastic process.