Solar steam generation has great potential in alleviating freshwater crises, particularly in regions with accessible seawater and abundant insolation. Inexpensive, efficient, and eco-friendly photothermal materials are desired to fabricate sunlight-driven evaporation devices. Here, we have designed an economical strategy to fabricate a high-performance wood-based solar steam generation device. In current study, 3D-hierarchical Cu3SnS4 has been loaded on wood substrates of variable sizes via an in-situ solvothermal method. Considering the water transportation capacity and thermal insulation property of wood, an enhanced light absorption was achieved by a uniform coating of Cu3SnS4 on the inside and outside of the 3D porous structure of the wood. Thanks for the synergistic effect of Cu3SnS4 and wood substrate, the obtained composite endorsed high-performance solar steam generation with a steam generation efficiency of 90% and an evaporation rate as high as 1.35 kg m-2h-1 under one sun.
This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.