Achieving thermally stable and anti-hydrolytic Sr2Si5N8:Eu2+ phosphor via a nanoscale carbon deposition strategy

Chemical stability of phosphors is critical to the efficiency and lifetime of the white light-emitting diodes. Therefore, many strategies have been adopted to improve the stability of phosphors. However, it is still lack of report on the improvement of thermal stability and hydrolysis resistance of phosphors by a single layer coating. Due to the high transmittance and high chemical inertness of graphene, it was coated on the surface of Sr₂Si₅N₈:Eu²⁺ phosphor by chemical vapor deposition, aiming to improve its thermal stability and hydrolysis resistance. The chemical composition and microstructure of the coating were characterized and analyzed. A nanoscale carbon layer was attached on the surface of Sr₂Si₅N₈:Eu²⁺ phosphor particles in an amorphous state. In coated Sr₂Si₅N₈:Eu²⁺ phosphor, the oxidation degree of Eu²⁺ to Eu³⁺ was significantly suppressed. At the same time, the surface of Sr₂Si₅N₈:Eu²⁺ particle turned from hydrophilic to hydrophobic after carbon coating, and consequently the hydrolysis resistance of Sr₂Si₅N₈:Eu²⁺ phosphor was greatly improved. After tests at 85 °C and 85% humidity for 200 h, the carbon coated Sr₂Si₅N₈:Eu²⁺ phosphor still maintained about 95% of its initial luminous intensity as compared with 35% of the uncoated. By observing the in-situ microstructure evolution of coated phosphor in air-water vapor environment, remained presence of the carbon layer even at 500 °C explained the excellent chemical stability of carbon coated Sr₂Si₅N₈:Eu²⁺ phosphor in complex environment. These results indicate that a nanoscale carbon layer can be used to provide superior thermal stability and hydrolysis resistance of (oxy) nitrides phosphors.     

A high-throughput chaotic advection microreactor for preparation of uniform and aggregated barium sulfate nanoparticles

A high-throughput (105.5 g/h) passive four-stage asymmetric oscillating feedback microreactor using chaotic mixing mechanism was developed to prepare aggregated Barium sulfate (BaSO₄) particles of high primary nanoparticle size uniformity. Three-dimensional unsteady simulations showed that chaotic mixing could be induced by three unique secondary flows (i.e., vortex, recirculation, and oscillation), and the fluid oscillation mechanism was examined in detail. Simulations and Villermaux–Dushman experiments indicate that almost complete mixing down to molecular level can be achieved and the prepared BaSO₄ nanoparticles were with narrow primary particle size distribution (PSD) having geometric standard deviation, σ_g, less than 1.43 when the total volumetric flow rate Q_total was larger than 10 ml/min. By selecting Q_total and reactant concentrations, average primary particle size can be controlled from 23 to 109 nm as determined by microscopy. An average size of 26 nm with narrow primary PSD (σ_g = 1.22) could be achieved at Q_total of 160 ml/min.     

CO2 sensing properties and mechanism of PrFeO3 and NdFeO3 thick film sensor

The CO_2 sensing of PrFeO_3 and NdFeO_3 sensors were investigated. Experimental results show that the resistances for PrFeO_3 and NdFeO_3 in CO_2 gas are larger than those in air and the responses for PrFeO_3and NdFeO_3 sensors increase with an increase in room-temperature relative humidity. When exposed to1000 ppm CO_2, the response of PrFeO_3 thick film based on nano-powders annealed at 700℃can reach8.44 at 160℃for the background of wet air with 58%of room-temperature relative humidity (RH),which is much larger than the corresponding value (3.03) in wet air with 25%RH. The sensing response S of NdFeO_3 thick-film sensor based on nano-powders annealed at 600℃to 3000 ppm CO_2 at the operating temperature 200℃can reach 2.36 for the background of wet air with 72%RH, which is larger than the corresponding value (1.83) in the air with 25%RH. Compared with other CO_2 sensing materials, the PrFeO_3 sensor has larger response at lower operating temperature for CO_2 gas and may be used as a new CO_2 sensing material.     

Elliptical distribution-based weight-determining method for ordered weighted averaging operators

The ordered weighted averaging (OWA) operators play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers’ choice. One key point steps is to determine the associated weights. In this paper, we first briefly review some main methods for determining the weights by using distribution functions. Then we propose a new approach for determining OWA weights by using the regular increasing monotone quantifier. Motivated by the idea of normal distribution-based method to determine the OWA weights, we develop a method based on elliptical distributions for determining the OWA weights, and some of its desirable properties have been investigated.     

基于视觉感知的智能扫地机器人的垃圾检测与分类

目的 为了提高扫地机器人的自主性和智能化程度，为扫地机器人配备视觉传感器，使其获得视觉感知能力，通过研究有效的垃圾检测分类模型与算法，实现对垃圾的定位与识别，引导扫地机器人对垃圾进行自动识别与按类处理，提高工作的目的性和效率，避免盲动和减少能耗。方法 选择检测速度较快的YOLOv2作为主网络模型，结合密集连接卷积网络，嵌入深层密集模块，对YOLOv2进行改进，提出一种YOLOv2-dense网络，该网络可以充分利用图像的高分辨率特征，实现图像浅层和深层特征的复用与融合。结果 测试结果表明，智能扫地机器人使用本文方法可以有效识别不同形态的常见垃圾类别，在真实场景中，测试识别准确率为84.98%，目标检测速度达到26帧/s。结论 实验结果表明，本文构建的YOLOv2-dense网络模型具有实时检测的速度，并且在处理具有不同背景、光照、视角与分辨率的图片时，表现出较强的适应和识别性能。在机器人移动过程中，可以保证以较高的准确率识别出垃圾的种类，整体性能优于原YOLOv2模型。     

Fabrication of poly(ε‐caprolactone) (PCL)/poly(propylene carbonate) (PPC)/ethylene‐α‐octene block copolymer (OBC) triple shape memory blends with cycling performance by constructing a co‐continuous phase morphology

In this paper, a triple shape memory material was prepared by ultra‐simple melt blending from poly(ε‐caprolactone) (PCL), poly(propylene carbonate) (PPC) and ethylene‐α‐octene block copolymer (OBC). The obtained material possessed a co‐continuous phase morphology and presented an excellent triple shape memory effect (triple‐SME). Theoretical prediction demonstrated that a special continuous phase morphology could be constructed by adjusting the proportions of the blend. Moreover, the results indicated that a close relationship existed between the phase morphology and the triple‐SME of PCL/PPC/OBC. The sample with 35 vol% PPC content contributed to the formation of a continuous phase morphology and exhibited the optimal triple‐SME. Additionally, the sample PCL/PPC/OBC (32.5/35/32.5) showed outstanding structure and performance stability during cycle loading–unloading tests, which evidenced the prominent cycling shape memory property (nearly 100% shape fixing and recovery of temporary shape). Overall, this work could provide an efficient, convenient and recyclable method to obtain high‐performance shape memory materials. © 2020 Society of Chemical Industry