Rechargeable aqueous Zn/manganese dioxide (Zn/MnO 2 ) batteries are attractive energy storage technology owing to their merits of low cost, high safety, and environmental friendliness. However, the b-MnO 2 cathode is still plagued by the sluggish ion insertion kinetics due to the relatively narrow tunneled pathway. Furthermore, the energy storage mechanism is under debate as well. Here, b-MnO 2 cathode with enhanced ion insertion kinetics is introduced by the efficient oxygen defect engineering strategy. Density functional theory computations show that the b-MnO 2 host structure is more likely for H + insertion rather than Zn 2+ , and the introduction of oxygen defects will facilitate the insertion of H + into b-MnO 2 . This theoretical conjecture is confirmed by the capacity of 302 mA h g À1 and capacity retention of 94% after 300 cycles in the assembled aqueous Zn/ b-MnO 2 cell. These results highlight the potentials of defect engineering as a strategy of improving the electrochemical performance of b-MnO 2 in aqueous rechargeable batteries.

As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.

Biphasic and multiphasic compounds have been well clarified to achieve extraordinary electrochemical properties as advanced energy storage materials. Yet the role of phase boundaries in improving the performance is remained to be illustrated. Herein, we reported the biphasic vanadate, that is, Na1.2V3O8/K2V6O16·1.5H2O (designated as Na0.5K0.5VO), and detected the novel interfacial adsorption–insertion mechanism induced by phase boundaries. First‐principles calculations indicated that large amount of Zn2+ and H+ ions would be absorbed by the phase boundaries and most of them would insert into the host structure, which not only promote the specific capacity, but also effectively reduce diffusion energy barrier toward faster reaction kinetics. Driven by this advanced interfacial adsorption–insertion mechanism, the aqueous Zn/Na0.5K0.5VO is able to perform excellent rate capability as well as long‐term cycling performance. A stable capacity of 267 mA h g−1 after 800 cycles at 5 A g−1 can be achieved. The discovery of this mechanism is beneficial to understand the performance enhancement mechanism of biphasic and multiphasic compounds as well as pave pathway for the strategic design of high‐performance energy storage materials.

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A simple carbonization of evaporation‐induced self‐assembled iron(III) porphyrin (FeP) layers uniformly coated on carbon black, leading to an unprecedented core/shell structured nonprecious metal electrocatalysts (NPMEs) composed of N‐doped graphene‐like layers uniformly coated on carbon is reported. The thickness of graphene‐like shell can be readily adjusted up to about 6.6 nm by varying the amount of FeP loaded on carbon. Interestingly, the obtained NPME exhibits one of the highest oxygen reduction reaction (ORR) activity in both alkaline (half‐wave potential of 0.87 V vs reversible hydrogen electrode‐RHE) and acidic (half‐wave potential of 0.75 V vs RHE) medium. In particular, the core/shell structured NPME demonstrates a remarkable durability in acidic conditions superior to that of commercial Pt/C, which likely comes from the exposure of inner active sites after the outermost layer is consumed. Furthermore, the core/shell NPME displays direct 4e and indirect 4e process toward ORR in alkaline and acidic medium, respectively. This study points out a new avenue for the design of high‐performance NPMEs in both alkaline and acidic media, which may have potential applications in polymer electrolyte membrane fuel cells (PEMFCs), metal‐air batteries, and electrolyzers.

While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (NMT) models for better translation quality remains a challenging problem. Directly stacking more blocks to the NMT model results in no improvement and even reduces performance. In this work, we propose an effective two-stage approach with three specially designed components to construct deeper NMT models, which results in significant improvements over the strong Transformer baselines on WMT14 English→German and English→French translation tasks 1 .

First principle calculations are employed to calculate the electronic and magnetic properties of Co doped MoS2 by considering a variety of defects including all the possible defect complexes. The results indicate that pristine MoS2 is nonmagnetic. The materials with the existence of S vacancy or Mo vacancy alone are non-magnetic either. Further calculation demonstrates that Co substitution at Mo site leads to spin polarized state. Two substitutional CoMo defects tend to cluster and result in the non-magnetic behaviour. However, the existence of Mo vacancies leads to uniform distribution of Co dopants and it is energy favourable with ferromagnetic coupling, resulting in an intrinsic diluted magnetic semiconductor.