An Intrinsically Nonflammable Electrolyte for Prominent-Safety Lithium Metal Batteries with High Energy Density and Cycling Stability

Nex-generation high-energy-density storage battery, assembled with lithium (Li)-metal anode and nickel-rich cathode, puts forward urgent demand for advanced electrolytes that simultaneously possess high security, wide electrochemical window, and good compatibility with electrode materials. Herein an intrinsically nonflammable electrolyte is designed by using 1 M lithium difluoro(oxalato)borate (LiDFOB) in triethyl phosphate (TEP) and N-methyl-N-propyl-pyrrolidinium bis(trifluoromethylsulfonyl)imide [Pyr₁₃][TFSI] ionic liquid (IL) solvents. The introduction of IL can bring plentiful organic cations and anions, which provides a cation shielding effect and regulates the Li⁺ solvation structure with plentiful Li⁺-DFOB⁻ and Li⁺-TFSI⁻ complexes. The unique Li⁺ solvation structure can induce stable anion-derived electrolyte/electrode interphases, which effectively inhibit Li dendrite growth and suppress side reactions between TEP and electrodes. Therefore, the LiNi_0.9Co_0.05Mn_0.05O₂ (NCM90)/Li coin cell with this electrolyte can deliver stable cycling even under 4.5 V and 60 °C. Moreover, a Li-metal battery with thick NCM90 cathode (≈ 15 mg cm⁻²) and thin Li-metal anode (≈ 50 µm) (N/P ≈ 3), also reveals stable cycling performance under 4.4 V. And a 2.2 Ah NCM90/Li pouch cell can simultaneously possess prominent safety with stably passing the nail penetration test, and high gravimetric energy density of 470 Wh kg⁻¹ at 4.4 V.     

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.     

基于改进免疫粒子群优化算法的室内可见光通信三维定位方法

针对室内可见光通信中3维定位精度不高和定位时间较长的问题,该文提出基于改进免疫粒子群(IIMPSO)算法的室内可见光通信(VLC)3维定位方法.通过分析室内多径效应,选取合适的视场角(FOV)以减少反射影响,同时完善了倾斜状态下的定位模型,并采用卡尔曼滤波算法以降低环境干扰对接收功率的影响,在此基础上与改进的免疫粒子群算法相融合.仿真结果表明,在5 m×5 m×3 m的室内环境中,该文所提出的3维定位系统平均定位误差为0.031 m,定位时长为2.3 s.与现有的3维定位系统进行比较,其定位精度与收敛速度均得到明显改善.     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.