Carbon corrosion mechanism on nitrogen-doped carbon support — A density functional theory study

In this work, density functional theory (DFT) calculations were used to investigate the mechanism of carbon corrosion on nitrogen-doped carbon support. Free energy diagrams were generated based on three proposed reaction pathways to evaluate corrosion mechanisms. The most energetically preferred mechanism on nitrogen-doped carbon was determined. The results show that the step of water dissociation to form ^#OH was the rate-determining step for gra-G-1N (graphene doped with graphitic N) and pyrr-G-1N (graphene doped with pyrrolic N). As for graphene doped with pyridinic N, the step of C^#OC^#O formation was critical. It was found that the control of nitrogen concentration was necessary for precisely designing optimized carbon materials. Abundance of nitrogen moieties aggravated the carbon corrosion. When the high potential was applied, specific types of graphitic N and pyridinic N were found to be favorable carbon modifications to improve carbon corrosion resistance. Moreover, the solvent effect was also investigated. The results provide theoretical insights and design guidelines to improve corrosion resistance in carbon support through material modification by inhibiting the adsorption of surface oxides (OH, O, and OOH).     

Effects on the crystallite growth behavior of LaPO4 nanopowders and its mechanical properties

The effects of pH of the reaction solution and the concentration of phosphoric acid on the crystal growth behavior of LaPO₄ crystallites were investigated and the mechanical properties of rare-earth phosphates were compared. As a result, the concentration of phosphoric acid of 10% was beneficial to the crystal growth of LaPO₄ nanocrystalline. When the pH value of the reaction solution was 2, the size of LaPO₄ crystallites increased gradually with the increasing reaction temperature, and the smallest crystallite size of 43.27 nm was obtained after heat-treatment at 1000 °C. Simultaneously, the activation energy for crystal growth of LaPO₄ nanocrystalline was relatively lower (26.82 kJ mol⁻¹). With the decreasing radii of rare-earth ions, the hardness, Young's modulus and fracture toughness of the bulk rare-earth phosphates exhibited a reduced tendency, resulted from the increase of porosity under the same preparation process.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

In2O3:Ga and In2O3:P-based one-electrode gas sensors: Comparative study

Gas sensing characteristics of one-electrode sensors based on the In₂O₃ ceramics doped by gallium and phosphorus have been discussed. In₂O₃-based ceramic was prepared by sol–gel technology. Ozone, CO, CH₄ and H₂ were used as tested gases. The doping concentration effect on the sensor parameters such as magnitude of response, operating temperature, response and recovery times, sensitivity to the air humidity, and selectivity have been analyzed. It was shown that In₂O₃ doping by Ga and P could be used for the sensor performance optimization. It was assumed that the appearance of the second phase (InPO₄ and Ga₂O₃) and the change of structural parameters, taking place during doping process, were the main factors controlling the change of operating characteristics in In₂O₃:P and In₂O₃:Ga-based sensors.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

High strength and high modulus carbon fibers

Carbon fibers have been processed from gel spun polyacrylonitrile copolymer on a continuous carbonization line at Georgia Tech (GT) with a tensile strength in the range of 5.5–5.8 GPa, and tensile modulus in the range of 354–375 GPa. This combination of strength and modulus is the highest for any continuous fiber reported to date, and the gel spinning route provides a pathway for further improvements in strength and modulus for mass production of carbon fibers. At short gauge length, fiber tensile strength was as high as 12.1 GPa, which is the highest value ever reported for a PAN based carbon fiber. Structure analysis shows random flaws of about 2 nm size, which results in limiting tensile strength of higher than 20 GPa. Inter-planar turbostratic graphite shear modulus in high strength carbon fibers is 30 GPa, while in graphite the corresponding value is only 4 GPa.     

Mitigating Hydrological Risk with Energy Derivatives

The variability of river inflows affects the energy production of hydropower generators and may result in reductions in revenues that can be financially disruptive for these producers. Recent climatic changes have highlighted the risks involved in hydroelectriciy production in Brazil. In this paper, we propose a different approach to formulating a collar derivative, namely an Inverted Collar, to mitigate hydrological risk considering the particularities of Brazil's energy regulatory environment. In addition, we propose a customized collar-by-difference as a variation of the collar model. The effect of these derivatives is analyzed considering electricity market price and power generation uncertainty for a typical hydro generator. The results suggest that these derivatives are effective tools to manage hydrological risk during period of great climatic volatility, such as the height of the drought period experienced by Brazil in 2016. The results also indicate that our models outperform traditional commercial hedging commonly practiced by hydropower producers in the country.     

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics.The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal.In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.     

Applications of data analysis techniques for oil production prediction

This paper describes two data analysis techniques adopted in a Decision Support System (DSS) that aids users in predicting oil production of an infill well. The system generates predictions in the form of a possible range of cumulative production and length of production life of an infill well. The system also shows the worst and best case scenarios based on different production curves so that the expert can examine the plots of predicted production rates for each existing well and decide which model gives the best fit. The production curve of each individual well was mathematically modeled so that production values beyond the historical data can be produced. Decline curve estimation and neural network approaches were adopted for data analysis in the system. The system was tested with data from two groups of wells from two different fields in Saskatchewan, Canada. Observations on the suitable duration that the historical data set should cover and a comparison among different curve estimation and neural network models are presented.     

Properties of gap-aggregate gradation asphalt mixture and permanent deformation

Aggregate of natural crush stone in hot mix asphalt is applied to gap gradation mixed with the AC-20 binder, in accordance with Marshall mix design. Laboratory tests were conducted, and the single-factor variance analysis (ANOVA) is employed to estimate the significance of the gradation mixture properties. Based on permanent deformation tests and analytical results, sieve of 2.36 mm may be omitted for nominal maximum aggregate size (NMAS) of 12.5 mm, and sieve of 4.75 mm may be omitted for NMAS of 19 mm, in the case of the shortage of aggregate.