基于纸喷雾离子化衍生质谱法快速检测PM2.5中的醌污染物

醌类化合物是PM2.5中的一类有害物质。本研究建立了纸喷雾离子化衍生质谱法快速测定PM2.5中的醌类污染物。通过衍生化反应在醌化合物中引入氨基，提高醌在纸喷雾中的离子化效率。随后对衍生化试剂种类、电压、喷雾溶剂种类等反应条件进行优化。在最优实验条件下，采用内标法定量分析1,4-苯醌、甲基对苯醌、1,4-萘醌和1,4-蒽醌，4种化合物均呈现较好的线性关系，其检出限分别为4.49、20.89、0.13、0.17 ng。利用该方法分析PM2.5实际样品中的萘醌和蒽醌，均获得了较好的定性和定量结果。     

2种侧围加强内框总成检具结构对比分析

概述了侧围加强内框总成与周边零件的匹配关系,从检具操作的人机工程出发,描述了侧围加强内框总成检具的2种定位方式和结构设计,对比了2种定位方式对于检测结果的影响,总结了其优点和弊端,结果表明侧围加强内框总成检具采用车内方向朝下的定位方式效果更好。     

One-step to prepare high-performance gas diffusion layer (GDL) with three different functional layers for proton exchange membrane fuel cells (PEMFCs)

Gas diffusion layer (GDL) is one of the most important components of fuel cells. In order to improve the fuel cell performance, GDL has developed from single layer to dual layers, and then to multiple layers. However, dual or multi layers in GDL are usually prepared by layer-by-layer methods, which cost too much time, energy, and resources. In this work, we successfully developed a facile one-step method to prepare a GDL with three functional layers by utilizing the different sedimentation rates and filtration rates of short carbon fiber (CF) and carbon nanotube (CNT). The treatment temperature for this GDL is much lower than that of traditional method. The thickness of the GDL can be effectively controlled from as thin as 50 μm to more than 200 μm by simply adjusting the content of CF. The GDL with high flexibility is suitable to develop high performance flexible electronics. The fuel cell with the GDL has the maximum power density 1021 mW cm^?2, which shows 19% improvement comparing to the conventional one. Therefore, this work breaks the traditional concept that GDL for fuel cells only can be prepared by very complex and high-cost procedure.     

Investigation of fabrication of gas diffusion substrate for proton exchange membrane fuel cells

The gas diffusion substrate (GDS) is essential in the proton exchange membrane fuel cells. Its fabrication techniques affect the performance significantly and are worthy of investigation. In this study, a manufacturing process of the GDS is proposed to understand the formation process of GDS and promote its structure and performance more pertinently. Different states during the preparation process, raw carbon paper, pre-curing, curing, carbonation, and graphitization, are characterized and measured. Experimental and numerical methods are employed to determine the relationships between microstructure, transport, and mechanical performance variation with the fabricating processes. The results show that its porosity, average pore size, and effective diffusivity decrease first and increase after curing. These parameters after graphitization are lower than that of the carbon paper (CP). The electrical resistivity increases dramatically while pre-curing and decreases gradually after curing, carbonation, and graphitization, and it is much reduced after graphitization. Moreover, mechanical measurement results show that both the picks of tensile strength and flexural modulus occur after curing. Its tensile strength shows little change after graphitization compared to the initial paper's. In contrast, the flexural modulus is improved significantly.     

成分配比对稀土钨电极材料组织及力学性能影响

通过固液掺杂、等静压压制、中频烧结的方法，制备了不同的氧化镧、氧化钇、氧化锆三元掺杂成分比例的钨电极材料烧结棒材，探究了不同成分配比对样品显微组织、第二相粒子分布以及宏观力学性能的影响。结果表明，氧化镧、氧化钇、氧化锆三元复合添加能够有效改善第二相粒子在钨基体中的分布形态，降低第二相在晶界的过度富集，提高钨电极材料的综合力学性能。并且当添加成分镧、钇、锆质量比为3:1:1时，材料具有最好的综合力学性能，致密度可达96.04%，显微硬度可达549.37HV0.3，抗压强度可达3785MPa，原因是此配比下第二相粒子最为细小均匀，弥散程度最高，对基体晶粒的细化作用最好，该配比下钨基体平均晶粒尺寸达到10.3μm。     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Moulded pulp products manufacturing with thermoforming

For an effective optimization of pulp thermoforming and of the moulded pulp products manufactured by this process, a full understanding of the process physics combined with full knowledge of the pressing equipment is necessary. For this reason, in this Addendum, we clarify how the process parameters “Holding time,” “Vacuum time,” “Cycle time,” and “Temperature” were interpreted and subsequently defined for the analysis of the process and product‐related outputs of the thermoforming experiments.     

Creep behaviour of an Al–Si–Al2O3 composite based on phase evolution at 1300oC

An Al–Si–Al₂O₃ composite was prepared with corundum, aluminium powder and silicon powder. A creep test was carried out at 1300°C under 0.2 MPa for 50 h in air. The results show that the Al–Si–Al₂O₃ composite performs a low constant creep rate and remain until the end of the 50-h test. This is attributed to the in-situ formation of the tough non-oxide reinforcements, whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC, by reactions of Al and Si during creep test. The whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC reinforcements are evenly filled in the pores, which play the role of bridging and pinning reinforcement, forming a strong network structure with corundum aggregates. Moreover, these non-oxide phases are not wetted by the liquid phases, which impel the liquid phase shrinks in the network structure in isolation during creep test. Thus, the adverse effect of the liquid phase on the high-temperature strength of the composites is eliminated, so the composites with strong network structure quickly get a stationary low-creep state. A creep mechanism model is established.     

Effects of SiC,Al2O3, and ZrO2 particles on the LBMed characteristics of Al/SiC,Al/Al2O3, and Al/ZrO2 MMCs prepared by stir casting process

The effects of SiC, Al₂O₃, and ZrO₂ particles on the characteristics of Al/SiC, Al/Al₂O₃, and Al/ZrO₂ metal matrix composites (MMCs) have been studied in the present research work. The comparison of machining characteristics has been done to analyze the behavior of various reinforced particles with the variation of laser machining variables. The output characteristics such as dross height and kerf deviation have been investigated and compared with each MMCs. SEM and XRD have been used for the investigation of morphological changes in the structure and agglomeration of reinforced particles. The crack and recast layer formation has been examined in the specimens of higher quantity of reinforced particles. It was observed that the MMC material reinforced with SiC particles has shown different behavior as compared to other MMC materials.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.