Toward High-Performance Dihydrophenazine-Based Conjugated Microporous Polymer Cathodes for Dual-Ion Batteries through Donor–Acceptor Structural Design

Recent studies have demonstrated that dihydrophenazine (Pz) with high redox-reversibility and high theoretical capacity is an attractive building block to construct p-type polymer cathodes for dual-ion batteries. However, most reported Pz-based polymer cathodes to date still suffer from low redox activity, slow kinetics, and short cycling life. Herein, a donor–acceptor (D–A) Pz-based conjugated microporous polymer (TzPz) cathode is constructed by integrating the electron-donating Pz unit and the electron-withdrawing 2,4,6-triphenyl-1,3,5-triazine (Tz) unit into a polymer chain. The D–A type structure enhances the polymer conjugation degree and decreases the band gap of TzPz, facilitating electron transportation along the polymer skeletons. Therefore the TzPz cathode for dual-ion battery shows a high reversible capacity of 192 mAh g⁻¹ at 0.2 A g⁻¹ with excellent rate performance (108 mAh g⁻¹ at 30 A g⁻¹), which is much higher than that of its counterpart polymer BzPz produced from 1,3,5-triphenylbenzene (Bz) and Pz (148 and 44 mAh g⁻¹ at 0.2 and 10 A g⁻¹, respectively). More importantly, the TzPz cathode also shows a long and stable cyclability of more than 10 000 cycles. These results demonstrate that the D–A structural design is an efficient strategy for developing high-performance polymer cathodes for dual-ion batteries.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

A novel active power quality compensator topology for electrified railway

To improve the power quality of traction power system, a novel active power quality compensator (APQC) and a new compensating currents detection method are proposed. The APQC consists of a three-phase voltage source converter and a Scott transformer. The Scott transformer, taken as an isolation transformer, not only connects the three-phase converter to the traction power system, but also converts the traction power system to a nearly balanced three-phase power system. Therefore, a general three-phase converter could be used in APQC. Regarding the traction substation as a compensating object, the power quality of a traction substation can be improved integrally. Simulation and prototype experimental results show that the proposed APQC is able to compensate reactive power, harmonic, and negative-sequence currents in two feeders of a traction substation.     

Enhancement of refresh time in quasi-nonvolatile memory by the density of states engineering

The recently reported quasi-nonvolatile memory based on semi-floating gate architecture has attracted extensive at-tention thanks to its potential to bridge the large gap between volatile and nonvolatile memory.However,the further exten-sion of the refresh time in quasi-nonvolatile memory is limited by the charge leakage through the p-n junction.Here,based on the density of states engineered van der Waals heterostructures,the leakage of electrons from the floating gate to the channel is greatly suppressed.As a result,the refresh time is effectively extended to more than 100 s,which is the longest among all pre-viously reported quasi-nonvolatile memories.This work provides a new idea to enhance the refresh time of quasi-nonvolatile memory by the density of states engineering and demonstrates great application potential for high-speed and low-power memory technology.     

IP网络及其实时业务展望

本文讨论了目前通信领域的２个热闹话题：ＩＰ网的网络组成和ＩＰ网上的实时业务。关于ＩＰ网的网络组成,本文讨论了ＩＰｏｖｅｒ ＡＴＭ．ＩＰ ｏｖｅｒ ＳＤＨ和ＩＰ ｏｖｅｒ ＷＤＭ技术。关于ＩＰ网上的实时业务,本文讨论了ＩＰ电话、ＩＰ会议和ＩＰ网的信息点播业务。     

Performance and reliability comparison between asymmetric andsymmetric LDD devices and logic gates

The performance and reliability of NMOSFET asymmetric lightly doped drain (LDD) devices (with no LDD on the source side) are compared with those of conventional LDD devices. At a fixed V_dd, asymmetric LDD devices exhibit higher I_dsat and shorter hot-carrier lifetime. To maintain the same hot-carrier lifetime, asymmetric LDD devices must operate at lower V_dd while higher I_dsat is retained. For the same hot-carrier lifetime, ring oscillators with NMOSFET asymmetric LDD devices can achieve 5% (10% if PMOSFET also had asymmetric LDD) higher speed and 10% lower power. The hot-carrier reliability of inverter, NAND, and NOR structures with asymmetric and conventional LDD devices is also simulated and compared     

基于压缩感知的随机噪声成像雷达

近年来提出的压缩感知(CS)理论指出可以从很少的采样点中以很大的概率准确重建原始的未知稀疏信号。该文将压缩感知与随机噪声雷达相结合,提出了基于压缩感知的随机噪声雷达,并给出了该雷达系统的基本原理框图,从理论上证明了基于压缩感知的随机噪声雷达的回波观测矩阵具有很好的等容性质,在目标场景稀疏或可以稀疏表示时,基于压缩感知的随机噪声雷达可以采集远小于常规随机噪声雷达成像所需的回波数据并能实现准确成像,最后通过仿真实验验证了该文的结论。     

Defect Passivation in Perovskite Solar Cells by Cyano‐Based π‐Conjugated Molecules for Improved Performance and Stability

Defects at the surface and grain boundaries of metal–halide perovskite films lead to performance losses of perovskite solar cells (PSCs). Here, organic cyano‐based π‐conjugated molecules composed of indacenodithieno[3,2‐b]thiophene (IDTT) are reported and it is found that their cyano group can effectively passivate such defects. To achieve a homogeneous distribution, these molecules are dissolved in the antisolvent, used to initiate the perovskite crystallization. It is found that these molecules are self‐anchored at the grain boundaries due to their strong binding to undercoordinated Pb²⁺. On a device level, this passivation scheme enhances the charge separation and transport at the grain boundaries due to the well‐matched energetic levels between the passivant and the perovskite. Consequently, these benefits contribute directly to the achievement of power conversion efficiencies as high as 21.2%, as well as the improved environmental and thermal stability of the PSCs. The surface treatment provides a new strategy to simultaneously passivate defects and enhance charge extraction/transport at the device interface by manipulating the anchoring groups of the molecules.     

Cellulose Nanofibrils Enhanced,Strong, Stretchable,Freezing‐Tolerant Ionic Conductive Organohydrogel for Multi‐Functional Sensors

To date, ionic conducting hydrogel attracts tremendous attention as an alternative to the conventional rigid metallic conductors in fabricating flexible devices, owing to their intrinsic characteristics. However, simultaneous realization of high stiffness, toughness, ionic conductivity, and freezing tolerance through a simple approach is still a challenge. Here, a novel highly stretchable (up to 660%), strong (up to 2.1 MPa), tough (5.25 MJ m^?3), and transparent (up to 90%) ionic conductive (3.2 S m^?1) organohydrogel is facilely fabricated, through sol–gel transition of polyvinyl alcohol and cellulose nanofibrils (CNFs) in dimethyl sulfoxide‐water solvent system. The ionic conductive organohydrogel presents superior freezing tolerance, remaining flexible and conductive (1.1 S m^?1) even at ?70 °C, as compared to the other reported anti‐freezing ionic conductive (organo)hydrogel. Notably, this material design demonstrates synergistic effect of CNFs in boosting both mechanical properties and ionic conductivity, tackling a long‐standing dilemma among strength, toughness, and ionic conductivity for the ionic conducting hydrogel. In addition, the organohydrogel displays high sensitivity toward both tensile and compressive deformation and based on which multi‐functional sensors are assembled to detect human body movement with high sensitivity, stability, and durability. This novel organohydrogel is envisioned to function as a versatile platform for multi‐functional sensors in the future.     

Incremental learning approach based on vector neural network for emitter identification

To deal with the problem of emitter identification (EID) caused by the measurement uncertainty of emitter feature parameters and to realise the automatic updating of the emitter database, which is usually used as emitter templates in identification processing, a vector neural network based incremental learning (VNNIL) approach for EID is proposed. This method combines the vector neural networks (VNNs) and the ensemble-based incremental learning (Learn++) algorithm. The VNN is adopted to construct a weak classifier and the Learn++ is used to generate ensembles of the weak classifiers. Considering that the VNN can realise the non-linear mapping between the interval-value input data and the interval-value output emitter types, and that the Learn++ can update the emitter database automatically, the VNNIL treats the two mentioned problems above as a single one and realises EID and parameters updating at the same time. A number of simulations are presented to demonstrate the identification and updating capability of the VNNIL algorithm. As shown in the simulation results, the VNNIL algorithm not only possesses a better learning and identification capability, but also achieves a better noise adaptability.