Effective utilization of abundant solar energy for desalination of seawater and purification of wastewater is one of sustainable techniques for production of clean water, helping relieve global water resource shortage. Herein, we fabricate a vertically aligned reduced graphene oxide/Ti3C2Tx MXene (A-RGO/MX) hybrid hydrogel with aligned channels as an independent solar steam generation device for highly efficient solar steam generation. The vertically aligned channels, generated by a liquid nitrogen-assisted directional-freezing process, not only rapidly transport water upward to the evaporation surface for efficient solar steam generation, but also facilitate multiple reflections of solar light inside the channels for efficient solar light absorption. The deliberate slight reduction endows the RGO with plenty of polar groups, decreasing the water vaporization enthalpy effectively and hence accelerating water evaporation efficiently. The MXene sheets, infiltrated inside the A-RGO hydrogel on the basis of Marangoni effect, enhance light absorption capacity and photothermal conversion performance. As a result, the A-RGO/MX hybrid hydrogel achieves a water evaporation rate of 2.09 kg·m−2·h−1 with a high conversion efficiency of 93.5% under 1-sun irradiation. Additionally, this photothermal conversion hydrogel rapidly desalinates seawater and purifies wastewater to generate clean water with outstanding ion rejection rates of above 99% for most ions.
Nutrient Cycling in Agroecosystems - Most studies on soil CO2 fluxes focus on the upper soil layers (i.e., 0–200 mm); however, there is a lack of investigation into soil layers below... 相似文献
Mechanical tea harvesting using plucking machines is highly efficient, but harvested raw fresh tea leaves (FTLs) are always low quality because they contain a mixture of old leaves and leaf debris. To address this problem, this study developed an automatic sorting machine with a vision-based recognition method to extract high-quality FTLs from plucked raw FTLs. First, the raw FTLs were separated one by one after passing through three sequential conveyor belts with increasing speed, and were then classified into four grades using a vision-based recognition method. Finally, the FTLs were blown by air nozzles into collection boxes according to their specific grade. In the recognition method, the shape-based feature of each FTL is extracted by establishing the FTL's topological structure, and the support vector machine model is used for classification. The experimental results revealed that the vision-based recognition method performed satisfactorily with an accuracy rate of 94% and precision rate of 85%. The sorting success rate and efficiency of the automatic sorting machine were approximately 80% and 15 kg hr−1, respectively. The results indicate that the developed automatic sorting machine can effectively and efficiently sort raw FTLs, which may improve the profitability and promote the automation of tea processing. 相似文献