Graphene with mediated surface properties and three-dimensional hierarchical architectures show unexpected performance in energy conversion and storage. To achieve advanced graphene electrode supercapacitors, manipulating the graphene building-blocks into hierarchical nanostructured carbon materials with large electrical double layer capacitance and pseudo-capacitance is a key issue. Here, it is shown that the hierarchically aminated graphitic honeycombs (AGHs) with large surface area for electrical double layer capacitance, tunable surface chemistry for pseudo-capacitance, mediated 3D macropores for ion buffering, and low-resistant pathways for ion diffusion are fabricated for electrochemical capacitive energy storage application through a facile high vacuum promoted thermal expansion and subsequent amination process. In the initial stage of amination (200 °C), NH3 reacts with carboxylic acid species to form mainly intermediate amides and amines through nucleophilic substitution. As the temperature increases, the intramolecular dehydration and decarbonylation will take place to generate thermally more stable heterocyclic aromatic moieties such as pyridine, pyrrole, and quaternary type N sites. The AGH exhibits a promising prospect in supercapacitor electrodes with high capacitance (e.g. maximum gravimetric capacitance 207 F g−1 and specific capacitance 0.84 F m−2 at a scan rate of 3 mV s−1) and extraordinary stability (e.g. 97.8% of capacitance retention after 3000 cycles, and 47.8% of capacitance maintaining at a high scan rate of 500 mV s−1 comparing with that at 3 mV s−1). This provides a novel structure platform for catalysis, separation, and drug delivery, which require fast mass transfer through mesopores, reactant reservoirs, and tunable surface chemistry

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Biome-specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome-specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network (ANN) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome-specific global annual Rs was the one that applied mean annual temperature (MAT), mean annual precipitation (MAP), and biome type as inputs (r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT (r 2 = 0.87) in the savannah biome. The developed biome-specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle.

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Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps. Can. J. Soil Sci. 88: 787Á799. High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within91 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within 91 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost.Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model Zhao, Z., Chow, T. L., Yang, Q., Rees, H. W., Benoy, B., Xing, Z. et Meng, F. R. 2008. Mode`le pre´visionnel des classes de drainage du sol d'apre`s les parame`tres du MAN et les attributs des cartes pe´dologiques a`faible re´solution. Can. J. Soil Sci. 88: 787Á799. Les cartes de drainage du sol a`haute re´solution reveˆtent une grande importance pour la planification des cultures, la gestion des foreˆts et l'e´valuation de l'environnement. Les cartes de classification des sols actuelles ont tendance a`ne fournir de renseignements que sur les principales conditions de drainage et ne conviennent pas a`la planification de la foresterie et de l'agriculture de pre´cision. L'e´tude devait cre´er un mode`le a`re´seau neuronal artificiel (RNA) permettant une classification a`haute re´solution du drainage du sol. Les auteurs ont examine´si on pouvait utiliser a`cette fin les donne´es sur le profil du sol tire´es des cartes a`faible re´solution (e´chelle 1:1 000 000) ainsi que les variables topographiques et hydrologiques du mode`le altime´trique nume´rique (MAN) (e´chelle 1:35 000). Ils ont ensuite utilise´la carte a`haute re´solution de drainage du sol (1:10 000) du bassin...

Lake Winnipeg eutrophication results from excess nutrient loading due to agricultural activities across the watershed. Estimating nonpoint-source pollution and the mitigation effects of beneficial management practices (BMPs) is an important step in protecting the water quality of streams and receiving waters. The use of computer models to systematically compare different landscapes and agricultural systems across the Red-Assiniboine basin has not been attempted at watersheds of this size in Manitoba. In this study, the Soil and Water Assessment Tool was applied and calibrated for three pilot watersheds of the Lake Winnipeg basin. Monthly flow calibration yielded overall satisfactory Nash-Sutcliffe efficiency (NSE), with values above 0.7 for all simulations. Total phosphorus (TP) calibration NSE ranged from 0.64 to 0.76, total N (TN) ranged from 0.22 to 0.75, and total suspended solids (TSS) ranged from 0.29 to 0.68. Based on the assessment of the TP exceedance levels from 1993 to 2007, annual loads were above proposed objectives for the three watersheds more than half of the time. Four BMP scenarios based on land use changes were studied in the watersheds: annual cropland to hay land (ACHL), wetland restoration (WR), marginal annual cropland conversion to hay land (MACHL), and wetland restoration on marginal cropland (WRMAC). Of these land use change scenarios, ACHL had the greatest impact: TSS loads were reduced by 33 to 65%, TN by 58 to 82%, and TP by 38 to 72% over the simulation period. By analyzing unit area and percentage of load reduction, the results indicate that the WR and WRMAC scenarios had a significant impact on water quality in high loading zones in the three watersheds. Such reductions of sediment, N, and P are possible through land use change scenarios, suggesting that land conservation should be a key component of any Lake Winnipeg restoration strategy.