Luyao Zhang

Since its debut in 2004, graphene has attracted enormous interest because of its unique properties. Chemical vapor deposition (CVD) has emerged as an important method for the preparation and production of graphene for various applications since the method was first reported in 2008/2009. In this Account, we review graphene CVD on various metal substrates with an emphasis on Ni and Cu. In addition, we discuss important and representative applications of graphene formed by CVD, including as flexible transparent conductors for organic photovoltaic cells and in field effect transistors. Growth on polycrystalline Ni films leads to both monolayer and few-layer graphene with multiple layers because of the grain boundaries on Ni films. We can greatly increase the percentage of monolayer graphene by using single-crystalline Ni(111) substrates, which have smooth surface and no grain boundaries. Due to the extremely low solubility of carbon in Cu, Cu has emerged as an even better catalyst for the growth of monolayer graphene with a high percentage of single layers. The growth of graphene on Cu is a surface reaction. As a result, only one layer of graphene can form on a Cu surface, in contrast with Ni, where more than one layer can form through carbon segregation and precipitation. We also describe a method for transferring graphene sheets from the metal using polymethyl methacrylate (PMMA). CVD graphene has electronic properties that are potentially valuable in a number of applications. For example, few-layer graphene grown on Ni can function as flexible transparent conductive electrodes for organic photovoltaic cells. In addition, because we can synthesize large-grain graphene on Cu foil, such large-grain graphene has electronic properties suitable for use in field effect transistors.

We report a simple, clean, and highly anisotropic hydrogen etching method for chemical vapor deposited (CVD) graphene catalyzed by the copper substrate. By exposing CVD graphene on copper foil to hydrogen flow around 800 °C, we observed that the initially continuous graphene can be etched to have many hexagonal openings. In addition, we found that the etching is temperature dependent. Compared to other temperatures (700, 900, and 1000 °C), etching of graphene at 800 °C is most efficient and anisotropic. Of the angles of graphene edges after etching, 80% are 120°, indicating the etching is highly anisotropic. No increase of the D band along the etched edges indicates that the crystallographic orientation of etching is in the zigzag direction. Furthermore, we observed that copper played an important role in catalyzing the etching reaction, as no etching was observed for graphene transferred to Si/SiO(2) under similar conditions. This highly anisotropic hydrogen etching technology may work as a simple and convenient way to determine graphene crystal orientation and grain size and may enable the etching of graphene into nanoribbons for electronic applications.

We report a vapor trapping method for the growth of large-grain, single-crystalline graphene flowers with grain size up to 100 μm. Controlled growth of graphene flowers with four lobes and six lobes has been achieved by varying the growth pressure and the methane to hydrogen ratio. Surprisingly, electron backscatter diffraction study revealed that the graphene morphology had little correlation with the crystalline orientation of underlying copper substrate. Field effect transistors were fabricated based on graphene flowers and the fitted device mobility could achieve ∼4200 cm(2) V(-1) s(-1) on Si/SiO(2) and ∼20 000 cm(2) V(-1 )s(-1) on hexagonal boron nitride (h-BN). Our vapor trapping method provides a viable way for large-grain single-crystalline graphene synthesis for potential high-performance graphene-based electronics.

Natural products of plant origin are of high interest and widely used, especially in the food industry, due to their low toxicity and wide range of bioactive properties. Compared to other plant components, the safety of polysaccharides has been generally recognized. As dietary fibers, plant-derived polysaccharides are mostly degraded in the intestine by polysaccharide-degrading enzymes secreted by gut microbiota, and have potential prebiotic activity in both non-disease and disease states, which should not be overlooked, especially in terms of their involvement in the treatment of intestinal diseases and the promotion of intestinal health. This review elucidates the regulatory effects of plant-derived polysaccharides on gut microbiota and summarizes the mechanisms involved in targeting gut microbiota for the treatment of intestinal diseases. Further, the structure-activity relationships between different structural types of plant-derived polysaccharides and the occurrence of their prebiotic activity are further explored. Finally, the practical applications of plant-derived polysaccharides in food production and food packaging are summarized and discussed, providing important references for expanding the application of plant-derived polysaccharides in the food industry or developing functional dietary supplements.

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.