Effect of mesoporous carbon on oxygen reduction reaction activity as cathode catalyst support for proton exchange membrane fuel cell

In this paper, a new carbon support with a large number of mesoporous-structures is selected to prepare Pt/C catalysts. Transmission electron microscope (TEM) results show that the Pt/3# catalyst presents a sponge-like morphology, Pt particles are not only evenly distributed on the surface of carbon support, but also the smaller Pt particles are deposited in the mesoporous inside the support. The average diameter of Pt particles is only 2.8 nm. The membrane electrode assembly (MEA) based on Pt/3# catalyst also shows excellent performance. In conclusion, the 3# support is an idea carbon support for PEMFC, which helps to improve the oxygen reduction reaction (ORR) activity of the catalyst. Based on the “internal-Pt” structure of the support mesoporous, the efficient three-phase boundaries (TPBs) are construct to avoid the poisoning effect of ionomer on the nano-metal particles, reduce the activation impedance and oxygen mass transfer impedance, and improve the reaction efficiency.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

承插型盘扣式支撑架在地铁车辆段工程中的应用

盘扣式支撑架具有承载力强、方便安全、结实耐用等优点,被广泛应用于地铁施工。本文以广州市轨道交通十八号线万顷沙车辆段为例,对承插型盘扣式支撑架在地铁车辆段工程中的具体应用进行了详细说明,以期为同类工程提供参考。     

Highly stable nanocarbon supported Pt catalyst for fuel cell via a molten salt graphitization strategy

Proton exchange membrane fuel cells (PEMFCs) durability has been severely hindered by carbon support poor stability in the cathodic Pt-based catalyst. Herein, a high-surface-area nitrogen-doped graphitic nanocarbon (N-G-CA) with mesopores is developed as Pt support to address PEMFCs durability challenge. Resorcinol-formaldehyde aerogel pyrolyzed carbon aerogel is selected as N-G-CA raw material. Nitrogen atoms are introduced into carbon aerogel via NH₃ heat treatment. Then, nitrogen-doped carbon aerogel is transferred into N-G-CA via heating together with transition-metal salts (one of FeCl₃, FeCl₂, CoCl₂, or MnCl₂, etc.) at 1200 °C. As ORR catalyst, Pt/N-G-CA half-wave potential only lost 10 mV, after 30, 000 cycles accelerated aging test in the rotating-desk-electrode. Only 12 mV voltage loss at 1.5 A/cm2 is observed, after 5, 000 cycles for membrane electrode. Pt/N-G-CA exhibits superior durability and activity than commercial Pt/C. High durability of Pt/N-G-CA is due to N-G-CA high graphitization extent, as well as the interactions between doping nitrogen and Pt. N-G-CA is promising as stable support for durable Pt-based catalysts in PEMFCs, thanks to enhanced carbon corrosion resistance, uniformly dispersed Pt, and strong support-metals interaction.     

基于FLAC3D的破碎围岩巷道支护效果分析

为了解破碎围岩分别采用锚杆支护、锚喷支护以及锚喷+锚索耦合三种支护方式下的支护效果,进而为破碎围岩巷道选择合理的支护方式提供参考。通过借助FLAC^3D软件建立数值模型,分析不同支护条件下的破碎围岩巷道位移量、应力分布以及塑性区的时空演化特征。结果表明,采用锚喷+锚索耦合支护时,可以较好的控制巷道围岩的位移量、减小应力集中效应、缩小塑性区的影响范围。     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

Utilizing sensor data to model students’ creativity in a digital environment

While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.