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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.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CSIRO-ARCCSS.ACCESS-CM2.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

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.

FROST summary outputs of the area burnt by short interval fires, high intensity fires, and overall area burnt of alpine ash stands in Victoria under various climate scenarios.

As atmospheric levels of CO(2) increase, reef-building corals are under greater stress from both increased sea surface temperatures and declining sea water pH. To date, most studies have focused on either coral bleaching due to warming oceans or declining calcification due to decreasing oceanic carbonate ion concentrations. Here, through the use of physiology measurements and cDNA microarrays, we show that changes in pH and ocean chemistry consistent with two scenarios put forward by the Intergovernmental Panel on Climate Change (IPCC) drive major changes in gene expression, respiration, photosynthesis and symbiosis of the coral, Acropora millepora, before affects on biomineralisation are apparent at the phenotype level. Under high CO(2) conditions corals at the phenotype level lost over half their Symbiodinium populations, and had a decrease in both photosynthesis and respiration. Changes in gene expression were consistent with metabolic suppression, an increase in oxidative stress, apoptosis and symbiont loss. Other expression patterns demonstrate upregulation of membrane transporters, as well as the regulation of genes involved in membrane cytoskeletal interactions and cytoskeletal remodeling. These widespread changes in gene expression emphasize the need to expand future studies of ocean acidification to include a wider spectrum of cellular processes, many of which may occur before impacts on calcification.

Premise: Seed recruitment niches along estuarine elevation gradients are seldom experimentally field-tested under tidal regimes of the Pacific Northwest of North America. Addressing this knowledge gap is important to better understand estuary restoration and plant community response to sea level rise. Methods: Germination was tested in marsh organ mesocosms across an elevation gradient (0.5–1.7 m above mean sea level). Seeds were sown on sterile peat moss, and the tops of pipes were secured with horticultural “frost cloth” to ensure no experimental seeds were washed out and no new seeds were introduced. The trials tested artificial and overwinter chilling regimes, as well as the presence and/or absence of a near-neighbor transplant. Results: Carex lyngbyei had significant elevation-driven germination after overwinter and artificial chilling. Schoenoplectus tabernaemontani had near-significant germination across elevation after overwinter chilling, and germination in the absence of competition was significantly greater than with a near-neighbor transplant. Discussion: Carex lyngbyei had the highest germination rate at higher elevations, which suggests restricted seed recruitment potential, and required clonal expansion to extend into lower marsh elevations. Identifying species-specific recruitment niches provides insight for restoration opportunities or invasive species monitoring, as well as for estuary migration under sea level rise. Datasets contain temperature data automatically recorded by iButton data loggers (iButtonLink, LLC, Whitewater, WI, USA. Ibuttonlink.com), or manually observed/recorded percent seed germination in experimental mesocosm (marsh organ). Please see ReadMe, then metadata files for each data file. R code is annotated with notes about analysis process, and often links to resources used to produce analyses.

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

This contains the monthly heating rate kernel based on ERA5 and data for "Diagnosing atmospheric heating rate changes using radiative kernels". Please find more details in README file.