A novel method for making ultra-fine metal fibers and particles

A novel method based on plastic processing and equipment for preparing ultra-fine metal fibers and particles is reported. With this new method, metal fibers and particles can both be produced on the same equipment and the surfaces of the fibers and particles can be protected from oxidation by the polymers or solvents during the preparation process. Metal-alloy powders with lower melt point were filled into polymer by an extruder, followed by a die-drawing process at a temperature lower than the melt temperature of the metal alloy. Metal fibers or particles were obtained after the polymer matrix was washed away. Metal alloy fibers can be obtained when a polymer that strongly interacts with metal alloy, such as a special polyvinyl alcohol with a low alcoholysis degree, is used as the polymer matrix. Metal-alloy particles can be obtained when a polymer with weak interaction with metal alloy, such as polyethylene (PE), is used as the polymer matrix. Based on the principle of this new method, it is possible to produce finer or even nano-sized metal fibers and particles with higher melting points.     

Synthesis and optical nondestructive evaluation of GaN nanorods on silicon surfaces with gold catalyst

Nanometric gallium-nitride rods were grown on a silicon (1 1 1) substrate through a chemical vapor deposition process with gold particles as the catalyst. Randomly distributed gallium-nitride rods of 20–200 nm in diameter and of various densities and lengths were formed under different deposition conditions. Characterization analyses, such as scanning electron microscopy and optical reflection spectroscopy, have been carried out on samples containing gallium-nitride rods different in size, shape, length and density. While the scanning electron microscopy shows directly the images of the sample surfaces, the optical spectroscopy provides a nondestructive evaluation of the sample surfaces, especially helpful for checking the uniformity of the samples.     

Convergence of a non-failable mean-field particle system

The existing literature contains many examples of mean-field particle systems converging to the distribution of a Markov process conditioned to not hit a given set. In many situations, these mean-field particle systems are failable, meaning that they are not well defined after a given random time. Our first aim is to introduce an original mean-field particle system, which is always well defined and whose large number particle limit is, in all generality, the distribution of a process conditioned to not hit a given set. Under natural conditions on the underlying process, we also prove that the convergence holds uniformly in time as the number of particles goes to infinity. As an illustration, we show that our assumptions are satisfied in the case of a piece-wise deterministic Markov process.     

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.     

A correlative classifiers approach based on particle filter and sample set for tracking occluded target

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.     

Tunable magnetophoretic method for distinguishing and separating wear debris particles in an Fe-PDMS-based microfluidic chip

Wear debris analysis provides an early warning of mechanical transmission system aging and wear fault diagnosis, which has been widely used in machine health monitoring. The ability to detect and distinguish the ferromagnetic and nonmagnetic debris in oil is becoming an effective way to assess the health status of machinery. In this work, an Fe-poly(dimethylsiloxane) (PDMS)-based magnetophoretic method for the continuous separation of ferromagnetic iron particles by diameter and the isolation of ferromagnetic particles and nonmagnetic particles with similar diameter by type is developed. The particles experience magnetophoretic effects when passing through the vicinity of the Fe-PDMS where the strongest gradient of the magnetic fields exists. By choosing a relatively short distance between the magnet and the sidewall of the horizontal main channel and the length of Fe-PDMS with controlled particles flow rate, the diameter-dependent separation of ferromagnetic iron particles, that is, smaller than 7 µm, in the range of 8–12 µm, and larger than 14 µm, and the isolation of ferromagnetic iron particles and nonmagnetic aluminum particles based on opposite magnetophoretic behaviors by types are demonstrated, providing a potential method for the detection of wear debris particles with a high sensitivity and resolution and the diagnostic of mechanical system.     

单颗粒光散射显微成像技术在生化医药分析中的应用

等离子体纳米颗粒(PNPs)具有体积小、易表面修饰、生物相容性好、毒性低等优点,在生物传感、生物成像、疾病诊断、肿瘤治疗、材料科学等领域得到了广泛的应用。PNPs的光散射光学性质可以通过调节其大小、组成、形貌和微环境来控制,可用于生化和药物分析。此外,由于单粒子散射显微技术具有高空间分辨率和高灵敏度,借助PNPs具有的独特局域表面等离子体共振(LSPR)特性,可在单颗粒水平进行实时成像。根据PNPs的大小、组成、形态、微环境或耦合变化引起的信号变化,研究人员发展了多种显微成像分析方法,主要分为4种,包括散射光谱的波长位移、单粒子散射强度的变化、高通量RGB分析和计数方法。基于纳米颗粒LSPR散射光谱位移变化的方法准确、灵敏,但需要昂贵的单颗粒散射光谱仪和复杂的操作。基于纳米颗粒散射强度变化的方法简单可行,但易受纳米颗粒粒径和曝光时间等因素的影响。高通量RGB分析方法灵敏度高、成本低,但不适用于颜色变化不明显的单颗粒分析,且重复性差。单粒子计数法灵敏度高,但有时粒子分布不均匀,背景杂质的干扰限制了方法的准确度。因此,这4种定量方法各有优缺点。此外,近年来逐渐发展了一些新的定量方法。例如,研究人员开发了新的时间分辨分析定量方法,并将暗场显微镜与偏振器、滤光片等光学器件相结合以消除背景干扰,以及与电化学、拉曼等仪器相结合以扩大应用范围。此外,为提高分析方法的准确度和灵敏度,暗场显微镜与深度学习、云计算、人工智能等现代计算机科学技术的结合,越来越受到人们的欢迎。基于以上原因,该文重点介绍了单粒子光散射显微镜在生化和药物分析领域的应用,总结了近年来的最新研究进展,讨论了单粒子光散射显微镜在定量分析中的几种主要定量方法,提出了未来的发展趋势,以期为相关研究领域的新人提供一定的学术参考。     

Accessing the spatiotemporal heterogeneities of single nanocatalysts by optically imaging gas nanobubbles

Catalysts that catalyze the generation of products in the gas phase, especially those involved in the hydrogen evolution reaction (HER), hold great promise for ecofriendly and sustainable energy development. In general, gas chromatography is widely used to measure catalytic activity. Unfortunately, it gives an averaged output that washes out the heterogeneities among individuals. To assess the unique catalytic properties at the single nanoparticle level, various methods based on single particle catalysis have been proposed. Over the past fifteen years, tremendous breakthroughs have been achieved, which uncovered hidden spatial and temporal heterogeneities. Although powerful, effectively quantifying the activities of single HER nanocatalysts remains challenging because of the fast diffusion of hydrogen (H₂). In 2017, a novel approach based on a nanobubble indicator was proposed to correlate the kinetics of gas bubble evolution with the catalytic activities of individual nanoentities during the HER process. Since then, a plethora of optical microscopy techniques have been utilized to monitor dynamically evolved nanobubbles and to measure the catalytic activities of single HER catalysts. In this minireview, we summarized state-of-the-art optical microscopy for in operando imaging of dynamic nanobubbles involved in gas-generating reactions while highlighting some important discoveries, including the blinking photocatalytic activity and heterogeneous distribution of active sites. Finally, challenges and future perspectives in this promising field were identified.     

KIT-6负载CeO2催化CO2和甲醇合成碳酸二甲酯

采用不同老化温度(80、100、120和150℃)合成了一系列KIT-6载体,并通过浸渍法制备了相应的CeO_2/KIT-6催化剂。结合X射线衍射、N_2物理吸附、NH_3程序升温脱附、CO_2程序升温脱附、透射电子显微镜、傅里叶变换红外光谱和X射线光电子能谱等表征结果,详细考察了老化温度对KIT-6结构以及CeO_2/KIT-6催化剂直接催化CO_2和甲醇合成碳酸二甲酯(DMC)反应活性的影响。结果表明,不同老化温度下制备的KIT-6均保持其独特的三维孔道结构。随着老化温度升高,KIT-6比表面积先增大后减小,当老化温度为100℃时,KIT-6比表面积达到最大(683 m~2·g~(-1))。KIT-6较高的比表面积有利于提高CeO_2分散度,进而提高暴露的活性位点数量,催化活性随催化剂表面中等碱/酸性吸附位数量和Ce~(3+)含量的增加而逐渐提高。其中,CeO_2/100-KIT-6催化剂中CeO_2颗粒尺寸最小(5.9 nm),暴露的活性位数量最高,催化活性最佳。随后,考察了反应温度和压力对CeO_2/100-KIT-6催化活性的影响。随着反应温度提高,催化活性先升高后降低,当反应温度为140℃时,催化活性最高;且催化活性随反应压力的提高而逐渐增加。在反应温度为140℃、压力为6.8 MPa条件下,催化剂经6次循环后,DMC收率由15 mmol·g_(CeO_2)~(-1)逐渐降低至2.8 mmol·g_(CeO_2)~(-1),原因归结为反应过程中CeO_2纳米颗粒发生团聚,使暴露出的活性位数量减少。     

ZIF-8材料的制备及应用进展

沸石咪唑酯骨架材料(Zeolitic Imidazolate Framework-8,ZIF-8)是由Zn（Ⅱ）与2-甲基咪唑配位自组装形成的多孔结晶材料,具有可调的孔径、高稳定的结构和催化活性等特点,近年来ZIF-8的制备和应用展现出巨大的潜力并引起了广泛关注。本文总结了目前ZIF-8的制备方法,在此基础上介绍了ZIF-8的形成机理及粒径调控方法,重点综述了ZIF-8及其复合材料在吸附分离、催化、生物医学等领域应用的研究进展,并展望了其应用前景与发展方向,以期为ZIF-8 的应用发展开拓新的思路。