Polyoxometalates (POMs) are widely used in catalysis, energy storage, biomedicine, and other research fields due to their unique acidity, photothermal, and redox features. However, the leaching and agglomeration problems of POMs greatly limit their practical applications. Confining POMs in a host material is an efficient tool to address the above‐mentioned issues. POM@host materials have received extensive attention in recent years. They not only inherent characteristics of POMs and host, but also play a significant synergistic effect from each component. This review focuses on the recent advances in the development and applications of POM@host materials. Different types of host materials are elaborated in detail, including tubular, layered, and porous materials. Variations in the structures and properties of POMs and hosts before and after confinement are highlighted as well. In addition, an overview of applications for the representative POM@host materials in electrochemical, catalytic, and biological fields is provided. Finally, the challenges and future perspectives of POM@host composites are discussed.
The dominant neural machine translation (NMT) models that based on the encoder-decoder architecture have recently achieved the state-of-the-art performance. Traditionally, the NMT models only depend on the representations learned during training for mapping a source sentence into the target domain. However, the learned representations often suffer from implicit and inadequately informed properties. In this paper, we propose a novel bilingual topic enhanced NMT (BLTNMT) model to improve translation performance by incorporating bilingual topic knowledge into NMT. Specifically, the bilingual topic knowledge is included into the hidden states of both encoder and decoder, as well as the attention mechanism. With this new setting, the proposed BLT-NMT has access to the background knowledge implied in bilingual topics which is beyond the sequential context, and enables the attention mechanism to attend to topic-level attentions for generating accurate target words during translation. Experimental results show that the proposed model consistently outperforms the traditional RNNsearch and the previous topic-informed NMT on Chinese-English and EnglishGerman translation tasks. We also introduce the bilingual topic knowledge into the newly emerged Transformer base model on English-German translation and achieve a notable improvement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.