Synergistic Adsorption and In Situ Catalytic Conversion of SO2 by Transformed Bimetal-Phenolic Functionalized Biomass.

SO2 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/CoOx nanoparticles through the thermal transition of metal-phenolic functionalized industrial leather wastes for synergistic SO2 adsorption and in situ catalytic conversion. The generation of surface-SO32- and peroxide species (O22-) by Ti/CoOx achieved catalytic conversion of adsorbed SO2 into value-added liquid H2SO4, which can be discharged from porous nanofibers. This approach can also avoid the accumulation of the adsorbed SO2, thereby achieving high desulfurization activity and a long operating life over 6000 min, preceding current state-of-the-art active carbon-based desulfurization materials. Combined with the techno-economic and carbon footprint analysis from 36 areas in China, we demonstrated an economically viable and scalable solution for real-world SO2 removal on the industrial scale.

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. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.

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. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test set to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter α=0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures.