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AbstractHigh‐performance dye‐sensitized solar cell (DSSC) devices rely on photoanodes that possess excellent light‐harvesting capabilities and high surface areas for sufficient dye adsorption. In this work, morphologically controlled SnO2 microstructures were synthesized and used as an efficient light‐backscattering layer on top of a nanocrystalline TiO2 layer to prepare a double‐layered photoanode. By optimizing the thickness of both the TiO2 bottom layer and the SnO2 top layer, a high power conversion efficiency (PCE) of 7.8 % was achieved, an enhancement of approximately 38 % in the efficiency compared with that of a nanocrystalline TiO2‐only photoanode (5.6 %). We attribute this efficiency improvement to the superior light‐backscattering capability of the SnO2 microstructures.

The many advantages of slug flow pneumatic conveying are outweighed by the lack of understanding of the flow mechanisms. For horizontal slug flow, the unique feature is the stationary layer of material found between the travelling slugs, which was recently shown to be characterised by two constants. This paper looks to utilise the vast data available in the literature, which is representative of the entire mode of flow, and relates the stationary layer and slug velocity to predict the two constants from only these inputs. It was found that, even for the vast range of materials and systems considered, slug flow encompasses a narrow bound of the two constants. Furthermore, an empirical approach that was developed to relate the layer fraction and particle velocity was found to provide good agreement to measurements and may be of use in other investigations that require an additional equation for modelling.

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.

A field study was conducted on two texturally different soils to determine the influences of biosolids application on selected soil chemical properties and carbon dioxide fluxes. Two sites, located in Manildra (clay loam) and Grenfell (sandy loam), in Australia, were treated at a single level of 70 Mg ha-1 biosolids. Soil samples were analyzed for SOC fractions, including total organic carbon (TOC), labile, and non-labile carbon contents. The natural abundances of soil δ13C and δ15N were measured as isotopic tracers to fingerprint carbon derived from biosolids. An automated soil respirometer was used to measure in-situ diurnal CO2 fluxes, soil moisture, and temperature. Application of biosolids increased the surface (0-15 cm) soil TOC by > 45% at both sites, which was attributed to the direct contribution from residual carbon in the biosolids and also from the increased biomass production. At both sites application of biosolids increased the non-labile carbon fraction that is stable against microbial decomposition, which indicated the soil carbon sequestration potential of biosolids. Soils amended with biosolids showed depleted δ13C, and enriched δ15N indicating the accumulation of biosolids residual carbon in soils. The in-situ respirometer data demonstrated enhanced CO2 fluxes at the sites treated with biosolids, indicating limited carbon sequestration potential. However, addition of biosolids on both the clay loam and sandy loam soils found to be effective in building SOC than reducing it. Soil temperature and CO2 fluxes, indicating that temperature was more important for microbial degradation of carbon in biosolids than soil moisture.

Abstract We evaluated three Integrated Assessment Models (IAMs: IGSM, MERGE, MiniCAM) by: (i) comparing their global Primary Energy year-2000 initializations and projections for 2010 and 2015 to historical data; (ii) mapping their CO2 emissions projections against observations; and (iii) examining model-output diagnostics. The IAMs underestimated historical primary energy consumption and initial/projected CO2 emissions in both reference and stabilization scenarios (particularly for combustion fuels) but overestimated usage of non-biomass renewables, causing underestimates of future CO2 emissions that, for the stabilization scenarios, are wildly optimistic. Mitigation technology breakdowns in the policy scenarios vary enormously across IAMs, suggesting that confidence in their projections might be misplaced, or that options for mitigation have greater scope than is supposed. Most increases in carbon-free technologies in the stabilization scenarios are already captured in the reference cases. Energy-conversion efficiencies in electricity generation improve over time, but, (except for gas-powered generation in IGSM), efficiencies in the policy scenarios are less than in the reference. Electrification results diverge widely: IGSM has little change over the 21st century, while MiniCAM and MERGE have major electrification increases in their policy scenarios. We suggest: 1) comprehensive model output suitable for secondary analysis should be more readily available; 2) directly comparable reference and policy-driven mitigation scenarios are essential for assessing mitigation measures; 3) model validation using historical, source-specific energy data is crucial for assessing model credibility; 4) separation of mitigation contributions into no-policy and policy-driven amounts is needed to assess the effectiveness of mitigation policies; and 5) detailed inter-model comparisons can provide important insights into model credibility.

AbstractMultinational conservation initiatives that prioritize investment across a region invariably navigate trade-offs among multiple objectives. It seems logical to focus where several objectives can be achieved efficiently, but such multi-objective hotspots may be ecologically inappropriate, or politically inequitable. Here we devise a framework to facilitate a regionally cohesive set of marine-protected areas driven by national preferences and supported by quantitative conservation prioritization analyses, and illustrate it using the Coral Triangle Initiative. We identify areas important for achieving six objectives to address ecosystem representation, threatened fauna, connectivity and climate change. We expose trade-offs between areas that contribute substantially to several objectives and those meeting one or two objectives extremely well. Hence there are two strategies to guide countries choosing to implement regional goals nationally: multi-objective hotspots and complementary sets of single-objective priorities. This novel framework is applicable to any multilateral or global initiative seeking to apply quantitative information in decision making.