Nanoporous Surface High-Entropy Alloys as Highly Efficient Multisite Electrocatalysts for Nonacidic Hydrogen Evolution Reaction

Electrocatalytic hydrogen evolution in alkaline and neutral media offers the possibility of adopting platinum-free electrocatalysts for large-scale electrochemical production of pure hydrogen fuel, but most state-of-the-art electrocatalytic materials based on nonprecious transition metals operate at high overpotentials. Here, a monolithic nanoporous multielemental CuAlNiMoFe electrode with electroactive high-entropy CuNiMoFe surface is reported to hold great promise as cost-effective electrocatalyst for hydrogen evolution reaction (HER) in alkaline and neutral media. By virtue of a surface high-entropy alloy composed of dissimilar Cu, Ni, Mo, and Fe metals offering bifunctional electrocatalytic sites with enhanced kinetics for water dissociation and adsorption/desorption of reactive hydrogen intermediates, and hierarchical nanoporous Cu scaffold facilitating electron transfer/mass transport, the nanoporous CuAlNiMoFe electrode exhibits superior nonacidic HER electrocatalysis. It only takes overpotentials as low as ≈240 and ≈183 mV to reach current densities of ≈1840 and ≈100 mA cm⁻² in 1 m  KOH and pH 7 buffer electrolytes, respectively; ≈46- and ≈14-fold higher than those of ternary CuAlNi electrode with bimetallic Cu–Ni surface alloy. The outstanding electrocatalytic properties make nonprecious multielemental alloys attractive candidates as high-performance nonacidic HER electrocatalytic electrodes in water electrolysis.     

Statistical Piezotronic Effect in Nanocrystal Bulk by Anisotropic Geometry Control

Utilizing inner-crystal piezoelectric polarization charges to control carrier transport across a metal-semiconductor or semiconductor–semiconductor interface, piezotronic effect has great potential applications in smart micro/nano-electromechanical system (MEMS/NEMS), human-machine interfacing, and nanorobotics. However, current research on piezotronics has mainly focused on systems with only one or rather limited interfaces. Here, the statistical piezotronic effect is reported in ZnO bulk composited of nanoplatelets, of which the strain/stress-induced piezo-potential at the crystals’ interfaces can effectively gate the electrical transport of ZnO bulk. It is a statistical phenomenon of piezotronic modification of large numbers of interfaces, and the crystal orientation of inner ZnO nanoplatelets strongly influence the transport property of ZnO bulk. With optimum preferred orientation of ZnO nanoplatelets, the bulk exhibits an increased conductivity with decreasing stress at a high pressure range of 200–400 MPa, which has not been observed previously in bulk. A maximum sensitivity of 1.149 µS m⁻¹ MPa⁻¹ and a corresponding gauge factor of 467–589 have been achieved. As a statistical phenomenon of many piezotronic interfaces modulation, the proposed statistical piezotronic effect extends the connotation of piezotronics and promotes its practical applications in intelligent sensing.     

Organic Synaptic Transistors: The Evolutionary Path from Memory Cells to the Application of Artificial Neural Networks

The progress of neural synaptic devices is experiencing an era of explosive growth. Given that the traditional storage system has yet to overcome the von Neumann bottleneck, it is critical to develop hardware with bioinspired information processing functions and lower power consumption. Transistors based on 2D materials, metal oxides, and organic materials have been adopted to mimic the synapse of a human brain, due to their high plasticity, parallel computing, integrated storage, and system information processing. Among these materials used to build transistors, organic semiconductors are considered to be the most promising candidate for neural synaptic devices and bio-electronics, owing to their easy processing, mechanical flexibility, low cost, good bio-compatibility, and ductility. This review focuses on the recent advances in organic synaptic devices with various structures, materials, and working mechanisms. The applications of artificial neural networks that integrate multiple organic synaptic transistors are also concretely discussed. Finally, the challenges that organic synaptic devices currently face are discussed and future developments are forecast.     

“Water-in-Deep Eutectic Solvent” Electrolytes for High-Performance Aqueous Zn-Ion Batteries

Aqueous Zn-ion batteries have been considered as promising alternatives to Li-ion batteries due to their abundant reserves, low price, and high safety. However, Zn anode shows poor reversibility and cycling stability in most conventional aqueous electrolytes. Here, a new type of aqueous Zn-ion electrolyte based on ZnCl₂–acetamide deep eutectic solvent with both environmental and economic friendliness has been prepared. The water molecule introduced in the “water-in-deep eutectic solvent” electrolyte could reduce the Zn²⁺ desolvation energy barrier by regulating Zn²⁺ solvation structure to promote uniform Zn nucleation. Zn anode shows improved electrochemical performance (≈98% Coulombic efficiency over 1000 cycles) in the electrolyte whose molar ratio of ZnCl₂:acetamide:H₂O is 1:3:1. The assembled full battery composed of phenazine cathode and Zn anode could stably cycle over 10 000 cycles with a high capacity retention of 85.7%. Overall, this work offers new insights into exploring new green electrolyte systems for Zn-ion batteries.     

Photomemory and Pulse Monitoring Featured Solution-Processed Near-Infrared Graphene/Organic Phototransistor with Detectivity of 2.4 × 1013 Jones

Emerging graphene/organic phototransistors are eye-catching technologies owing to their unique merits including easy/low-cost fabrication, temperature independent, and achieving various functions. However, their development in the near-infrared (NIR) region is experiencing a bottleneck of inferior sensitivity due to low exciton dissociation efficiency and inefficient charge extraction rate. Here, a novel-design solution-processed graphene/organic NIR phototransistor is reported, that is, creatively introducing electron extraction layer of ZnO on graphene channel and employing organic ternary bulk heterojunction as photosensitive layer, successfully breaking that bottleneck. The phototransistor exhibits a high responsivity of 6.1 × 10⁶ A W⁻¹, a superior detectivity of 2.4 × 10¹³ Jones, and a remarkable minimum detection power of 1.75 nW cm⁻² under 850 nm radiation. Considering its excellent NIR detection performance, a noncontact transmission-type pulse monitoring is carried out with no external circuit support, from which human pulse signal and heart rate can be displayed in real time. The phototransistor, interestingly, can be switched into a photomemory function with a retention time of 1000 s in the atmosphere through a gate voltage of −20 V. The design takes the characteristics of graphene/organic phototransistors to a higher level, beyond the limit of sensitivity, and opens up a novel approach for developing multifunction devices.     

Wood-Inspired Binder Enabled Vertical 3D Printing of g-C3N4/CNT Arrays for Highly Efficient Photoelectrochemical Hydrogen Evolution

2D nanomaterials are very attractive for photoelectrochemical applications due to their ultra-thin structure, excellent physicochemical properties of large surface-area-to-volume ratios, and the resulting abundant active sites and high charge transport capacity. However, the application of commonly used 2D nanomaterials with disordered-stacking is always limited by high photoelectrode tortuosity, few surface-active sites, and low mass transfer efficiency. Herein, inspired by wood structures, a vertical 3D printing strategy is developed to rapidly build vertically aligned and hierarchically porous graphitic carbon nitride/carbon nanotube (g-C₃N₄/CNT) arrays by using lignin as a binder for efficient photoelectrochemical hydrogen evolution. Arising from the directional electron transport and multiple light scattering in the out-of-plane aligned and porous architecture, the resulting g-C₃N₄/CNT arrays display an outstanding hydrogen evolution performance, with the hydrogen yield up to 4.36 µmol (cm⁻² h⁻¹) at a bias of −0.5 V versus RHE, 12.7 and 41.6 times higher than traditional thick g-C₃N₄/CNT and g-C₃N₄ films, respectively. Moreover, this 3D printed structure can overcome the agglomeration problem of the commonly used g-C₃N₄ with powder configuration and shows desirable recyclability and stability. This facile and scalable vertical 3D printing strategy will open a new avenue to highly enhance the photoelectrochemical performance of 2D nanomaterials for sustainably production of clean energy.     

Photoactive Electro-Controlled Visual Perception Memory for Emulating Synaptic Metaplasticity and Hebbian Learning

In bionic technology, it has become an innovative process imitating the functionality and structuralism of human biological systems to exploit advanced artificial intelligent machines. Bionics plays a significant role in environmental protection, especially for its low energy loss. By fusing the concept of receptor-like sensing component and synapse-like memory, the photoactive electro-controlled optical sensory memory (PE-SM) is proposed and realized in a single device, which endows a simple methodology of reducing power consumption by photoactive electro-control. The PE-SM is the system built with the stacked atomically thick materials, in which rhenium diselenide serves as a robust photosensor, hexagonal boron nitride serves as a tunneling dielectric, and graphene serves as a charge-storage layer. With the features of the PE-SM, it performs synaptic metaplasticities under optical spikes. In addition, a simulated spiking neural network composed of 24 × 24 PE-SMs is further presented in an unsupervised machine learning environment, performing image recognition via the Hebbian rule. The PE-SM not only improves the neuromorphic computing efficiency but also simplifies the circuit-size structure. Eventually, the concept of photoactive electro-control can extend to other photosensitive 2D materials and provide a new approach of constructing either visual perception memory or photonic synaptic devices.     

K-PSO: An improved PSO-based container scheduling algorithm for big data applications

In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes-based particle swarm optimization(K-PSO). Experimental results show that the proposed K-PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K-PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K-PSO algorithm is about 20% higher than that of the Kube-scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.     

节能灯灯头扭矩测试仪器的改进

针对目前广泛使用的节能灯灯头扭矩测试仪器采用人手直接施加扭矩的缺陷进行改进。     

悬浮粒子浓度测定的实验室间比对试验分析

通过对检测实验室间悬浮粒子浓度的比对试验进行分析,以准确了解参加比对的人员和仪器的工作状况,验证相关实验室悬浮粒子浓度的检测能力,确保空气洁净度检测结果的质量.