Mo2C Electrocatalysts for Kinetically Boosting Polysulfide Conversion in Quasi-Solid-State Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) suffer from sluggish reaction kinetics of sulfur-containing species and loss of soluble polysulfides (PSs) during cycling, especially in the case of liquid electrolytes. Here, we improve the kinetics of sulfur species by decorating Mo2C nanoparticles on carbon nanotubes (CNTs) as the host for sulfur active mass. In addition, by use of gel polymer electrolytes (GPEs) derived from in situ polymerization of 1,3-dioxolane (DOL) to mitigate the diffusion of PSs and improve the stability of Li stripping/plating. As a result, the sulfur cathodes are endowed with enhanced initial specific capacity and suppressed dissolution of sulfur species. The cells with CNT/Mo2C/S cathodes and GPE exhibit excellent electrochemical performance. The anodes cycled with GPE show remarkably enhanced lithium plating-stripping behavior. Benefitting from the synergistic effect, LSBs with higher energy density and improved durability are obtained, demonstrating a new approach for developing high-performance quasi-solid-state Li metal batteries.

[Pollution characteristics and ozone formation potential of ambient VOCs in winter and spring in Xiamen].

Air samples were collected at urban and rural sites in Xiamen from January to April 2014. The concentrations of 48 ambient volatile organic compounds (VOC) species were measured by the method of cryogenic pre-concentrator and gas chromatography-mass spectrometry (GC/MS). The ozone formation potential (OFP) of VOCs was also calculated with the method of maximum incremental reactivity (MIR). The results showed that the average mixing ratios of VOCs in winter were 11.13 x 10(-9) and 7.17 x 10(-9) at urban and rural sites, respectively, and those in spring were 24.88 x 10(-9) and 11.27 x 10(-9) at urban and rural sites, respectively. At both sites, alkanes contributed the most to VOCs, followed by aromatics and alkenes. The ratios of B/T showed that vehicle and solvent evaporation were the main sources of VOCs at urban site. While at rural site, transport of anthropogenic sources was another important source of VOCs besides local biomass emissions. Ten main components including propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, benzene, toluene, ethylbenzene and m/p-xylene accounted for 61.57% and 45.83% of total VOCs at urban and rural sites in winter, respectively, and 62.83% and 53.74% at urban and rural sites in spring, respectively. Aromatics contributed the most to total OFP, followed by alkenes. Alkanes contributed the least to OFP with the highest concentration. C3, C4 alkenes and aromatics were found to be the more reactive species with relatively high contributions to ozone formation in Xiamen. Comparing the average MIR of VOCs at the two sites, it was found that the reactivity of VOCs at rural site was higher than that at urban site.

[Spatial and temporal variations of near surface atmospheric CO2 with mobile measurements in fall and spring in Xiamen, China].

The study on the spatial distribution of near surface air pollutants carbon dioxide (CO2) and particulate matters (PM) is essential for understanding the pollution characteristics with mobile measurements. Near surface concentrations of CO2, PM and meteorological parameters were measured in Xiamen city, China along the route passing through different functional areas using the mobile laboratory during different time periods of the day [09:00- 12: 00, 13 :00- 16 : 00, 22 : 00-01 : 00 (local time) ] in spring (April) and fall (November), 2013. Carbon dioxide, PM and meteorological parameters data were analyzed for the spatial distribution of CO2 in different functional areas and the relationship of CO2, and PM2.5. During the study period, the measurements started at the northern part of the city, across the suburban area and ended at about 60 km in the southern Xiamen. The spatial distribution of CO2 along the road showed a high CO2 level in the central area of the city and low values in the outlying areas. Different CO2 concentrations were observed at different functional areas because of the differences in emissions from traffic and industry, the emission and absorption by vegetation, and meteorological conditions. The concentrations of CO, at different areas fell into the following order: areas with heavy traffic (477.33 micromol.mol-1 +/- 6. 11 micromol.mol-1 ) > commercial residential area (454. 95 micromol.mol-1 +/- 5.45 micromol.mol-1 ) > the naturalscenic spot (441.01 micromol.mol-1 +/- 6.24 micromol.mol-1 ) >cultivated land (436.79 micromol.mol-1 +/- 1.87 micromol.mol-1 ) > mountain woodlands (434.06 micromol.mol-1 +/-0.31 micromol.mol-1 ). The average CO, concentration in spring 2013 was measured to be 452.04 micromol mol -1 +/- 20.24 micro.mol. mol-1 with the maximum value of 533.10 micromol.mol-1 at the heavy traffic area in downtown Jiahe on April 12, 2013 and the minimum value of 413.25 micromol.mol-1 on April 10, 2013 at the mountain woodland, which is about 23 km away from the downtown area. The mountain woodland is surrounded by a reservoir and woods and regarded as the background area. The average CO2 concentration in fall 2013 was determined to be 451.80 micromol.mol-1 +/-21.56 micromol.mol-1 with the maximum value of 526.45 micromol.mol-1 at a heavy traffic area of Xiahe road in downtown, on November 19, 2013 and the minimum value of 415.01 p.mol.mol-' at the mountain woodland on November 10, 2013. This phenomenon was called "CO2 dome" by Idso in 1998. In addition, the CO2 concentrations tended to be the highest at night (22:00-01:00) and the lowest in afternoon (13:00-16:00). During overcast days, the CO, concentrations were generally higher than those on clear days. At different functional areas, differences between nighttime (22:00-01:00) and daytime (09:00-12:00 and 13:00-16:00) ranged from -0. 66-29.48 micromol.mol-1 in spring and from -4.01 micromol.mol-1-33.69 micromol.mol-1 in fall. The CO2 concentrations at the urban and the suburban areas were also different in spring and autumn and at different time of the day. The CO2 concentration was in significant correlation with PM2.5 (R =0.73, P < 0. 01) indicating the important impact of traffic pollution on the ambient CO2 concentration.