铁氧化物在流化床温度和CO气氛下的形态迁移及其对NO催化还原

利用小型固定床实验台实验研究了铁氧化物在典型流化床温度和CO还原性气氛下的形态迁移及其生成物对NO的催化还原作用，采用分级还原结合X射线衍射（XRD）表征分析，确定铁氧化物与CO和NO反应后生成物的价态及各种铁氧化物对NO的还原机制。结果表明，Fe₂O₃在实验条件下可依次被CO还原为Fe₃O₄、FeO和单质铁，反应过程中随着还原度的增加，还原速率逐级下降，从Fe₂O₃还原到Fe₃O₄的速率最高，FeO还原到Fe速率最低，在实验温度范围内，床温升高有利于提高Fe₂O₃到Fe₃O₄的还原速率和还原度。不同形态的铁氧化物对NO的催化还原特性不同，Fe₂O₃及其部分还原后生成的Fe₃O₄都不能直接与NO反应，Fe₂O₃对CO催化还原NO的效果很弱，而Fe₃O₄对CO还原NO的反应却有很强的催化作用，而进一步还原生成FeO与单质铁还可直接与NO反应。     

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.     

Mn3O4 nanosheets coated on carbon nanotubes as efficient electrocatalysts for oxygen reduction reaction

MnO-MnC_x coated carbon nanotubes (MnO/MnC_x/CNTs) nanocomposites were prepared by a one-pot deposition method. The coating consisted of MnO, Mn₅C₂, Mn₁₅C₄ and Mn₂₃C₆ was formed on the surface of CNTs by heating a mixture of Mn particles and CNTs at 600 °C for 40 min under vacuum. Then after heated MnO/MnC_x/CNTs in air at 350 °C for 2 h, MnO nanoparticles were partially converted to Mn₃O₄ nanosheets. Then Mn₃O₄-MnC_x coated carbon nanotubes (Mn₃O₄/MnC_x/CNTs) composed of interconnected nanosheets structure were successfully synthesized by a two-step method of one-pot deposition and heat post-treatment. The Mn₃O₄/MnC_x/CNTs showed better oxygen reduction reaction performance in alkaline condition than MnO/MnC_x/CNTs and pristine CNTs. Besides, the formed MnC_x (Mn₅C₂ and Mn₂₃C₆) by one-pot deposition method provided a strong interface bonding between Mn₃O₄ and CNTs, leading to improved stability of Mn₃O₄/MnC_x/CNTs as an electrode material.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Self-assembled cubic-hexagonal perovskite nanocomposite as intermediate-temperature solid oxide fuel cell cathode

Self-assembly is an emerging strategy for preparing composite cathodes with good oxygen electrochemical reduction activity and congenital chemical compatibility for intermediate-temperature solid oxide fuel cell (IT-SOFC). Here we report that a self-assembled BaCo_0.6Zr_0.4O_3-δ (BZC-BC) nanocomposite is prepared through one-pot glycine-nitrate process and exhibits high cathode performance. The BZC-BC nanocomposite is composed of 62 mol% cubic perovskite BaZr_0.82Co_0.18O_3-δ (BZC) as an ionic conductor and 38 mol% hexagonal perovskite BaCo_0.96Zr_0.04O_2.6+δ (12H-BC) as a mixed ionic and electronic conductor. The BZC-BC nanocomposite has the pomegranate-like particles aggregated with a larger number of nanoparticles (50-100 nm) which greatly enlarge the three-phase boundary sites. The BZC-BC nanocomposite exhibits a thermal expansion coefficient of 12.89 × 10⁻⁶ K⁻¹ well-matched with that of Ce_0.8Gd_0.2O_3-δ (12.84 × 10⁻⁶ K⁻¹) electrolyte. The high electro-catalytic activity of BZC-BC nanocomposite cathode for oxygen reduction is reflected by the low polarization resistances of oxygen ions incorporation at cathode/electrolyte interface (0.02823 Ω cm²), oxygen species diffusion (0.03702 Ω cm²) and oxygen adsorptive dissociation (0.07609 Ω cm²) at 700 °C. The single cell with BZC-BC nanocomposite cathode achieves the maximum power density of 1094 mW cm⁻² at 650 °C and shows good stability under 25 h run.     

选矿摇床智能巡检机器人开发与应用

摇床受处理量、给矿浓细度等条件变化影响,床面各条矿带的颜色、位置、宽度时常改变,需要人工及时调整接矿板的位置以达到合格的精矿品位,工人劳动强度大。为了实现摇床作业的自动巡检和操作,通过计算机视觉技术采集摇床矿带照片,研究了矿带图像识别算法,并在此基础上开发了摇床智能巡检机器人。工业试验表明,摇床智能巡检机器人实现了对一组多张摇床矿带的自动采图,图像识别算法实现了对矿带宽度、边界和颜色特征的数字化识别解析,实现了自动调节接矿板至目标位置,达到了替代人工、提升选矿指标的目的。     

用于机床减振的磁流变阻尼器优化设计

以三平动冗余驱动并联机床为研究对象,设计了一种具有位移放大功能的磁流变阻尼器,通过Ansys workbench有限元分析软件,对阻尼器的磁路饱和情况进行模拟仿真和分析,使阻尼器的阻尼出力达到最优。研究结果表明,通过优化使磁场分布更加合理,提高了阻尼器的力学性能,优化方法是可行有效的。     

Tuning morphology and structure of Fe–N–C catalyst for ultra-high oxygen reduction reaction activity

Exploring efficient and durable non-precious metal catalysts for oxygen reduction reaction (ORR) has long been pursued in the field of metal-air batteries, fuel cells, and solar cells. Rational design and controllable synthesis of desirable catalysts are still a great challenge. In this work, a novel approach is developed to tune the morphologies and structures of Fe–N–C catalysts in combination with the dual nitrogen-containing carbon precursors and the gas-foaming agent. The tailored Fe–N₁/N₂–C-A catalyst presents gauze-like porous nanosheets with uniformly dispersed tiny nanoparticles. Such architectures exhibit abundant Fe-N_x active sites and high-exposure surfaces. The Fe–N₁/N₂–C-A catalyst shows extremely high half-wave potential (E_1/2, 0.916 V vs. RHE) and large limiting current density (6.3 mA cm⁻²), far beyond 20 wt% Pt/C catalyst for ORR. This work provides a facile morphological and structural regulation to increase the number and exposure of Fe-N_x active sites.