Solvent-dependent evolution of cyclic penta-twinned rhodium icosahedral nanocrystals and their enhanced catalytic properties

Cyclic penta-twinned noble metal nanocrystals exhibit promising properties due to their unique geometric and electronic structures. However, the controlled synthesis of cyclic penta-twinned nanostructures, especially of noble metals with a high cohesive energy (e.g., Rh), is very difficult, and the corresponding growth mechanism is not fully understood. Herein, we report a facile one-pot hydrothermal approach for the synthesis of cyclic penta-twinned Rh icosahedral nanocrystals. It was found that apart from regulating the surface free energy by changing the concentration or category of the capping agents, the solvent might influence the adsorption ability of the surfactant on the Rh crystal surface, which results in a change in the surface free energy and thus allows the formation of Rh cyclic penta-twinned nanostructures. In addition, due to their unique electronic and geometric structures, the Rh icosahedral nanocrystals exhibit superior catalytic activity and stability for the electrooxidation of ethanol as compared to single-crystal Rh tetrahedral nanocrystals and commercial Rh black.

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Layered double hydroxides with atomic-scale defects for superior electrocatalysis

Atomic composition tuning and defect engineering are effective strategies toenhance the catalytic performance of multicomponent catalysts by improvingthe synergetic effect; however, it remains challenging to dramatically tune the active sites on multicomponent materials through simultaneous defect engineeringat the atomic scale because of the similarities of the local environment. Herein,using the oxygen evolution reaction (OER) as a probe reaction, we deliberatelyintroduced base-soluble Zn(II) or Al(III) sites into NiFe layered double hydroxides(LDHs), which are one of the best OER catalysts. Then, the Zn(II) or Al(III) siteswere selectively etched to create atomic M(II)/M(III) defects, which dramaticallyenhanced the OER activity. At a current density of 20 mA·cm^?2, only 200 mV overpotential was required to generate M(II) defect-rich NiFe LDHs, which is the best NiFe-based OER catalyst reported to date. Density functional theory(DFT) calculations revealed that the creation of dangling Ni–Fe sites (i.e., unsaturated coordinated Ni–Fe sites) by defect engineering of a Ni–O–Fe site at the atomic scale efficiently lowers the Gibbs free energy of the oxygen evolutionprocess. This defect engineering strategy provides new insights into catalysts atthe atomic scale and should be beneficial for the design of a variety of catalysts.

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Nano functional neural interfaces

Engineered functional neural interfaces (fNIs) serve as essential abiotic–biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms. fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.     

In situ trapped high-density single metal atoms within graphene: Iron-containing hybrids as representatives for efficient oxygen reduction

Nano Research - Atomically dispersed catalysts have attracted attention in energy conversion applications because their efficiency and chemoselectivity for special catalysis are superior to those...     

基于RTP/RTCP的塔机远程实时监控系统设计与实现

塔机远程安全监控模型容易受到电缆线或无线电信号的限制,大大降低塔机信号传递距离和质量,导致传统的塔机监控模型信号仅能传递几十米到几百米,无法实现塔机的远程实时监控,设计并实现了一种基于RTP/RTCP的智能塔机远程实时监控系统,采用基于C/S与B/S结合的数据链驱动无缝集成模式,设计了智能塔机远程监控模型,实现了各模块间的网络传递,主要包括嵌入式监控终端、中心服务器以及远程客户端3个模块,对中心服务器和客户端采用RTP/RTCP实现视频传递,设计了基于Ajax的Web交互式人机界面,对大规模网络数据进行实时处理分析,最终完成了塔机远程实时视频传输的过程;实验结果说明,所设计模型能够实现塔机的远程实时智能监控,具有较高的监控效率和精度。     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

环肋圆柱壳在流场中的辐射声压

应用Ｈｕｙｇｅｎｓ原理和Ｇｒｅｅｎ函数方法推出了环肋圆柱壳在流场中辐射声场压力的积分公式,可用来计算壳体表面、近场和远场的声压。     

Steady-state limiting currents determined by coupled diffusion, migration, and chemical equilibrium

A simple theoretical model is presented for the reduction of a singly charged cation under conditions where migration is important and the cation is coupled to a neutral species through a chemical equilibrium, AB = A(+) + B(-). Only the steady-state transport-limited current, I(l), is considered. Simple algebraic equations describe the ratio of I(l) to the diffusion-limited current, I(d), as it depends on the degree of dissociation, determined by the ratio of equilibrium constant to formal concentration, K(AB)/C*(AB). The ratio I(l)/I(d) is found to depend on the ratio of electrolyte to equilibrium concentration of A(+) in bulk solution just as for the well-known result for the case without the equilibrium (i.e., K(AB) → ∞). The results are in accord with published experimental data for weak acids. Agreement and disagreement with other theoretical treatments of this problem are discussed. The main results are for 1:1 supporting electrolytes; extensions are made to 2:1, 1:2, and 2:2 supporting electrolytes.     

Three-map absolute moiré contouring

In the shadow-moiré system, the period of the grating is varied by rotation of the grating, so the phase of the moiré pattern is changed as well. By the selection of suitable rotation angles, three images at different positions of the grating are acquired to obtain the absolute distance from the object to the grating. A theoretical analysis is presented for the method, and some experiments have been done to verify the theoretical analysis. The results show that the method is fast and the accuracy is better than 10 μm. The measurable range is directly proportional to the period of the grating and inversely proportional to the angles at which the grating is rotated.     

Adaptive control of harmonic drive with parameter varying friction using structurally dynamic wavelet network

In this paper, an adaptive controller with structurally dynamic wavelet network is developed for a harmonic drive subject to parameter varying friction. The control architecture integrates a proportional controller, a feedback adaptive component and sliding component to adaptively compensate for the friction to achieve accurate position tracking. Global asymptotic stability of the algorithm is proved by using Lyapunov function. In parallel to the adaptive controller, a fuzzy reconfiguration scheme is devised to change the structure of the network along with weights updating to improve the system tracking performance and robustness. Experimental tests on a harmonic drive manipulator verify the effectiveness of the proposed control method.