No abstract

In this paper, a green one-step strategy is developed to fabricate three-dimensional (3D) graphene-based multifunctional material with the aid of tannic acid. Tannic acid (TA), a typical plant polyphenol widely present in woods, reduced GO and induced the self-assembly of reduced graphene oxide into graphene hydrogel. The preparation process was carried out in aqueous media under atmosphere pressure without using any toxic reducing agent or special instrument, which is a facile, green, and low-cost method. The as-prepared monolithic 3D graphene exhibits high porosity, low density, hydrophobicity, good mechanical performance, and thermal stability. In addition, it shows excellent adsorption toward dyes, oils, and organic solvent, which should be a promising candidate for efficient adsorbents in water purification. Moreover, the tannic acid retained in the skeleton of 3D graphene functions as a biofunctional component, which endows the TA-GH with good antibacterial capability.

No abstract

No abstract

No abstract

Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the nal result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term in uence of current selection on subsequent decisions. We conduct experiments on di erent types of datasets, the results show that our model outperforms state-of-the-art systems and has be er generalization performance.

No abstract

No abstract