Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

Solid Solvation Assisted Electrical Doping Conserves High-Performance in 500 nm Active Layer Organic Solar Cells

Organic solar cells (OSCs) process fascinating solution-printing capability to achieve low-cost and large-scale manufacture. However, the rapid power conversion efficiency (PCE) decay with active layer thickness enlargement inhibits the implement of OSCs’ potential advantages. To overcome the bottlenecks of PCE decay in thick active layer OSCs, the electrical doping with componential selectivity in bulk heterojunction (BHJ) film is achieved by introducing a solid solvation additive. Benefiting from the higher exciton splitting efficiency together with the longer drift (L_dr) and diffusion (L_diff) lengths, an OSC with 100 nm BHJ film demonstrates a PCE increment from 16.44% to 18.24% with prolonged dark and illuminated storage stabilities. Applying the solid solvation assisted (SSA) doping method in the OSCs with 500 nm active layer, the PCE significantly increases by 31.9%, from the original value of 11.79% to 15.55%. It further improves to 15.84% in a ternary blend thick-film device, which is the record value to the best of our knowledge. Besides, the SSA doping narrows the PCE gap between the 0.04 and 1 cm² devices. All improvements demonstrate the great potential of SSA doping for OSC commercial manufacture, since it optimizes the photovoltaic performance under all practical conditions of long-term, thick-film, and large-area.     

Energy Transfer Induced by TADF Polymer Enables the Recycling of Excitons in Perovskite Solar Cells

Formamidinium lead triiodide (FAPbI₃) has been demonstrated as the most efficient perovskite system to date, due to its excellent thermal stability and an ideal bandgap approaching the Shockley-Queisser limit. Whereas, there are intrinsic quantum confinement effects in FAPbI₃, which lead to unwanted non-radiative recombination. Additionally, the black α-phase of FAPbI₃ is unstable under room temperature due to the significant residual tensile stress in the film. To simultaneously address the above issues, a thermally-activated delayed fluorescence polymer P1 is designed in the study to modify the FAPbI₃ film. Owing to the spectral overlap between the photoluminescence of P1 and absorption of the above-bandgap quantum wells of FAPbI₃, the Förster energy transfer occurs at the P1/FAPbI₃ interface, which further triggers the Dexter energy transfer within FAPbI₃. The exciton “recycling” can thus be realized, which reduces the non-radiative recombination losses in perovskite solar cells (PSCs). Moreover, P1 is found to introduce compressive stress into FAPbI₃, which relieves the tensile stress in perovskite. Consequently, the PSCs with P1 treatment achieve an outstanding power conversion efficiency (PCE) of 23.51%. Moreover, with the alleviation of stress in the perovskite film, flexible PSCs (f-PSCs) also deliver a high PCE of 21.40%.     

Simultaneous CO2 and H2O Activation via Integrated Cu Single Atom and N Vacancy Dual-Site for Enhanced CO Photo-Production

Photocatalytic conversion of CO₂ into fuels using pure water as the proton source is of immense potential in simultaneously addressing the climate-change crisis and realizing a carbon-neutral economy. Single-atom photocatalysts with tunable local atomic configurations and unique electronic properties have exhibited outstanding catalytic performance in the past decade. However, given their single-site features they are usually only amenable to activations involving single molecules. For CO₂ photoreduction entailing complex activation and dissociation process, designing multiple active sites on a photocatalyst for both CO₂ reduction and H₂O dissociation simultaneously is still a daunting challenge. Herein, it is precisely construct Cu single-atom centers and two-coordinated N vacancies as dual active sites on CN (Cu₁/N_2CV-CN). Experimental and theoretical results show that Cu single-atom centers promote CO₂ chemisorption and activation via accumulating photogenerated electrons, and the N_2CV sites enhance the dissociation of H₂O, thereby facilitating the conversion from COO* to COOH*. Benefiting from the dual-functional sites, the Cu₁/N_2CV-CN exhibits a high selectivity (98.50%) and decent CO production rate of 11.12 µmol g⁻¹ h⁻¹. An ingenious atomic-level design provides a platform for precisely integrating the modified catalyst with the deterministic identification of the electronic property during CO₂ photoreduction process.     

A Hybrid Strategy-Based Ultra-Narrow Stretchable Microelectrodes with Cell-Level Resolution

Stretchable ultra-narrow (e.g., 10 µm in width) microelectrodes are crucial for the electrophysiological monitoring of single cells providing the fundamental understanding to the working mechanism of neuro network or other electrically functional cells. Current fabrication strategies either focus on the preparation of normal stretchable electrodes with hundreds of micrometers or millimeters in width by using inorganic conductive materials or develop conductive organic polymer gel for ultra-narrow electrodes which suffer from low stretchability and instability for long-term implantation, therefore, it is still highly desirable to explore bio-interfacial ultra-narrow stretchable inorganic electrodes. Herein, a hybrid strategy is reported to prepare ultra-narrow multi-channel stretchable microelectrodes without using photolithography or laser-assisting etching. A 10 µm × 10 µm monitoring window is fabricated with enhanced interfacial impedance by the special rough surface. The stretchability achieves to 120% for this 10 µm-width stretchable electrode. Supported by these superior properties, it is demonstrated that the stretchable microelectrodes can detect electrophysiological signals of single cells in vitro and collect electrophysiological signals more precisely in vivo. The reported strategy will open up the accessible preparation of the fine-size stretchable microelectrode. It will significantly improve the resolution of monitoring and stimulation of inorganic stretchable electrodes.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

基于机器学习的超声造影分析综述

超声造影（Contrast-enhanced ultrasound, CEUS）通过外周静脉注入超声造影剂,显著增强来自肿瘤微血管的血流信号,便于临床医生以实时、动态的方式评估肿瘤血管生成、周边浸润等,广泛应用于多器官病变诊断、预后评估和治疗方案规划等方面。近年来,以深度学习为代表的机器学习方法快速发展,为动态超声造影智能分析带来新的机遇。深度学习方法很大程度上拓宽了超声造影临床应用范围,提高了其诊疗效能。但与常规超声影像类似,超声造影仍然存在斑点噪声、呼吸运动干扰和标准化程度低等问题,使得动态灌注时间、空间信息挖掘面临挑战。本文系统性回顾了近年来超声造影智能分析相关工作,涵盖良恶性鉴别、恶性分级、疗效预测和诊疗方案选择等方面应用,总结了当前影像组学及深度学习方法在超声造影分析领域的最新进展,并指出当前研究的局限性和未来发展方向。     

Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.      

结合数据增强和改进 YOLOv4 的水下目标检测算法

针对水下低质量成像、水下目标形态大小各异、以及水下目标重叠或遮挡导致水下目标检测精度低的问题,提出一种结合数据增强和改进YOLOv4(you look only once)的水下目标检测算法,在YOLOv4的主干特征提取网络CSPDarknet53中添加卷积块注意力机制(convolutional block attention module, CBAM),以提高网络模型特征提取能力;在路径聚合网络(path aggregation network, PANet)中添加同层跳接和跨层跳接结构,以增强网络模型多尺度特征融合能力;通过数据增强方法PredMix(prediction-mix)模拟水下生物重叠、遮挡等显示不完全的情形,以增强网络模型鲁棒性。实验结果表明,结合数据增强和改进YOLOv4的水下目标检测算法在URPC2018(underwater robot picking control 2018)数据集上的检测精度提升到了78.39%,比YOLOv4高出7.03%,充分证明所提算法的有效性。     

多源域子域自适应的滚动轴承剩余寿命预测方法

针对单一源域信息有限、域自适应对齐粒度不足导致滚动轴承剩余寿命( remain useful life, RUL)预测精度低的问题, 提出了一种多源域子域自适应(multi-source subdomain adaption network, MS_SAN)的滚动轴承剩余寿命预测方法。 首先,将采 集的原始振动信号进行快速傅里叶变换得到频域信号作为模型的输入。 其次,利用一维卷积将多个源域与目标域数据映射到 一个公共的特征空间,采用局部最大均值差异将每个源域与目标域的退化阶段在独立的特征空间进行领域自适应,缩小多个源 域与目标域之间的分布差异。 最后,通过综合各领域 RUL 预测模块的输出得到最终轴承剩余寿命预测结果。 在 PHM2012 数 据集上的测试结果表明该方法的预测准确率高于对比方法,能够对滚动轴承剩余寿命进行有效的预测。