Low shear rheological behaviour of two-phase mesophase pitch

The low shear rate rheology of two phase mesophase pitches derived from coal tar pitch has been investigated. Particulate quinoline insolubles (QI) stabilised the mesophase spheres against coalescence. Viscosity measurements over the range 10–10⁶ Pa s were made at appropriate temperature ranges. Increasing shear thinning behaviour was evident with increasing mesophase content. At low mesophase contents the dominant effect on the near Newtonian viscosity was temperature but at higher contents it was the shear rate; temperature dependence declined to near zero. The data indicated that agglomeration could be occurring at intermediate mesophase volume fractions, 0.2–0.3. The Krieger–Dougherty function and its emulsion analogue indicated that in this region the mesophase pitch emulsions actually behaved like ‘hard’ sphere systems and the effective volume fraction was estimated as a function of shear rate illustrating the change in extent of agglomeration. At the higher volume fractions approaching the maximum packing fraction, which could only be measured at higher temperatures, the shear thinning behaviour changed in character and it is considered that this is possibly due to shear induced deformation and breakup of dispersed drops in the shear field.     

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.     

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.     

Willingness to pay and preferences for alternative incentives to EV purchase subsidies: An empirical study in China

The transport sector's promotion of electric vehicles (EVs) is an important tool in reducing greenhouse gas emissions. The high monetary subsidies widely used to promote EV diffusion in many countries are not sustainable in the long term. Therefore, effective alternative incentives are needed as subsidies are gradually phased out. In this paper, consumers' willingness to pay (WTP) for alternative incentives is studied based on a questionnaire survey. Using a discrete choice experiment, 1719 Chinese consumer questionnaires were collected. Consumers' WTP for nine alternative policies was calculated by applying the multinomial logit model and mixed logit model, and the mixed logit model is used as the main model of this study because of its better statistical performance. Finally, the relationship between the respondent heterogeneity and policy preferences was studied. The results show that alternative incentives can fill the gap produced by subsidy reductions. EV privileges like no restrictions on driving and purchases, which have the lowest implementation cost, are suitable for first- and second- tier cities. For second-tier cities, construction of more charging stations is the most appropriate choice. The heterogeneous features of the respondents, including cities of residence, type of vehicle owned, recognition of the environmental benefits of EVs, gender, and whether the family has children, had a significant impact on policy preferences.     

Carbon-based nanomaterials for removal of chemical and biological contaminants from water: A review of mechanisms and applications

In recent years, significant discoveries related to antimicrobial and adsorption properties of carbon-based nanomaterials have led to new avenues for removal of various biological and organic/inorganic contaminants in drinking water. Furthermore, progress in the synthesis of multifunctional nanocomposites paves the way for their application in advanced water treatment system design. This review article describes and compares the adsorptive and antimicrobial properties of four common classes of carbon nanomaterials: single- and multi-walled carbon nanotubes, graphene, and graphene oxide, as well as some of their most important polymeric and metallic nanocomposites. Barriers for application of these nanomaterials in sustainable water treatment are also addressed.     

ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

Mechanical properties of graphene films enhanced by homo-telechelic functionalized polymer fillers via π–π stacking interactions

Most researches on graphene/polymer composites are focusing on improving the mechanical and electrical properties of polymers at low graphene content instead of paying attention to constructing graphene’s macroscopic structures. In current study the homo-telechelic functionalized polyethylene glycols (FPEGs) were tailored with π-orbital-rich groups (namely phenyl, pyrene and di-pyrene) via esterification reactions, which enhanced the interaction between polyethylene glycol (PEG) molecules and chemical reduced graphene oxide (RGO) sheets. The π–π stacking interactions between graphene sheets and π-orbital-rich groups endowed the composite films with enhanced tensile strength and tunable electrical conductivity. The formation of graphene network structure mediated by the FPEGs fillers via π–π stacking non-covalent interactions should account for the experimental results. The experimental investigations were also complemented with theoretical calculation using a density functional theory. Atomic force microscope (AFM), scanning electron microscope (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), thermal gravimetric analysis (TGA), UV–vis and fluorescence spectroscopy were used to monitor the step-wise preparation of graphene composite films.     

Selective oxidation of glycerol over nitrogen-doped carbon nanotubes supported platinum catalyst in base-free solution

In this communication, N-doped multiwall carbon nanotube (N-MWCNT) supported Pt NPs (1.8 nm) were prepared via a facile routine under microwave irradiation and tested in the selective oxidation of glycerol in an aqueous base-free solution. Characterizations confirmed that N-MWCNTs could improve the dispersion of Pt through strengthened metal-support interactions and donate its electron to metallic Pt. This electron-enriched Pt NPs on the surface of N-MWCNTs is active and stable for the selective oxidation of glycerol.