Tea/Coffee Sustainable Nanoarchitectures Purify Wastewater.

Transforming spent coffee grounds and tea residues into valuable hierarchical porous materials presents a sustainable solution for environmental remediation due to the low cost, extensive availability, and versatile functionalized interface. Here, we systematically investigated tea polyphenol-mediated morphological transformation of spent coffee grounds to the synthesis of three-dimensional (3D) mesoporous metal-organic framework (MOF)-derived nanoarchitectured carbon composites. We adopted the sustainable cost-effective tea-coffee derivative to remove typical marine micropollutants, such as antibiotic wastewater, radioactive pollutants, and microplastics.

A surrogate-assisted extended generative adversarial network for parameter optimization in free-form metasurface design.

Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise.

Synergistic Adsorption and In Situ Catalytic Conversion of SO by Transformed Bimetal-Phenolic Functionalized Biomass.

SO removal is critical to flue gas purification. However, based on performance and cost, materials under development are hardly adequate substitutes for active carbon-based materials. Here, we engineered biomass-derived nanostructured carbon nanofibers integrated with highly dispersed bimetallic Ti/CoO nanoparticles through the thermal transition of metal-phenolic functionalized industrial leather wastes for synergistic SO adsorption and in situ catalytic conversion.

The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.

Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening.

Deep Learning-Enabled Resolution-Enhancement in Mini- and Regular Microscopy for Biomedical Imaging.

Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy.