Building 3D structures of vanadium pentoxide nanosheets and application as electrodes in supercapacitors

Various two-dimensional (2D) materials have recently attracted great attention owing to their unique properties and wide application potential in electronics, catalysis, energy storage, and conversion. However, large-scale production of ultrathin sheets and functional nanosheets remains a scientific and engineering challenge. Here we demonstrate an efficient approach for large-scale production of V2O5 nanosheets having a thickness of 4 nm and utilization as building blocks for constructing 3D architectures via a freeze-drying process. The resulting highly flexible V2O5 structures possess a surface area of 133 m(2) g(-1), ultrathin walls, and multilevel pores. Such unique features are favorable for providing easy access of the electrolyte to the structure when they are used as a supercapacitor electrode, and they also provide a large electroactive surface that advantageous in energy storage applications. As a consequence, a high specific capacitance of 451 F g(-1) is achieved in a neutral aqueous Na2SO4 electrolyte as the 3D architectures are utilized for energy storage. Remarkably, the capacitance retention after 4000 cycles is more than 90%, and the energy density is up to 107 W.h.kg(-1) at a high power density of 9.4 kW kg(-1)

Building 3D Structures of Vanadium Pentoxide Nanosheets and Application as Electrodes in Supercapacitors

Various two-dimensional (2D) materials have recently attracted great attention owing to their unique properties and wide application potential in electronics, catalysis, energy storage, and conversion. However, large-scale production of ultrathin sheets and functional nanosheets remains a scientific and engineering challenge. Here we demonstrate an efficient approach for large-scale production of V₂O₅ nanosheets having a thickness of 4 nm and utilization as building blocks for constructing 3D architectures via a freeze-drying process. The resulting highly flexible V₂O₅ structures possess a surface area of 133 m² g^–1, ultrathin walls, and multilevel pores. Such unique features are favorable for providing easy access of the electrolyte to the structure when they are used as a supercapacitor electrode, and they also provide a large electroactive surface that advantageous in energy storage applications. As a consequence, a high specific capacitance of 451 F g^–1 is achieved in a neutral aqueous Na₂SO₄ electrolyte as the 3D architectures are utilized for energy storage. Remarkably, the capacitance retention after 4000 cycles is more than 90%, and the energy density is up to 107 W·h·kg^–1 at a high power density of 9.4 kW kg^–1

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat

peer reviewedThe climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.AgMIP - Agricultural model intercomparison and improvement projec