NO removal from exhausted gas is necessary owing to its damage to environment. Meanwhile, the electrochemical ammonia synthesis (EAS) from N2 suffers from low reaction rate and Faradaic efficiency (FE). Now, an alternative route for ammonia synthesis is proposed from exhaust NO via electrocatalysis. DFT calculations indicate electrochemical NO reduction (NORR) is more active than N2 reduction (NRR). Via a descriptor‐based approach, Cu was screened out to be the most active transition metal catalyst for NORR to NH3 owing to its moderate reactivity. Kinetic barrier calculations reveal NH3 is the most preferred product relative to H2, N2O, and N2 on Cu. Experimentally, a record‐high EAS rate of 517.1 μmol cm−2 h−1 and FE of 93.5 % were achieved at −0.9 V vs. RHE using a Cu foam electrode, exhibiting stable electrocatalytic performances with a 100 h run. This work provides an alternative strategy to EAS from exhaust NO, coupled with NO removal.

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For most metal-containing CO 2 reduction reaction (CO 2 RR) electrocatalysts, the unavoidable self-reduction to zero-valence metal will promote hydrogen evolution, hence lowering the CO 2 RR selectivity. Thus it is challenging to design a stable phase with resistance to electrochemical self-reduction as well as high CO 2 RR activity. Herein, we report a scenario to develop hydrocerussite as a stable and active electrocatalyst via in situ conversion of a complex precursor, tannin-lead(II) (TA-Pb) complex. A comprehensive characterization reveals the in situ transformation of TA-Pb to cerussite (PbCO 3), and sequentially to hydrocerussite (Pb 3 (CO 3) 2 (OH) 2), which finally serves as a stable and active phase under CO 2 RR condition. Both experiments and theoretical calculations confirm the high activity and selectivity over hydrocerussite. This work not only offers a new approach of enhancing the selectivity in CO 2 RR by suppressing the self-reduction of electrode materials, but also provides a strategy for studying the reaction mechanism and active phases of electrocatalysts.

Recently, electrochemical NO reduction (eNORR) to ammonia has attracted enormous research interests due to the dual benefits in ammonia synthesis and denitrification fields. Herein, taking Ag as a model catalyst, we have developed a microkinetic model to rationalize the general selectivity trend of eNORR with varying potential, which has been observed widely in experiments, but not understood well. The model reproduces experiments well, quantitatively describing the selectivity turnover from N2O to NH3 and from NH3 to H2 with more negative potential. The first turnover of selectivity is due to the thermochemical coupling of two NO* limiting the N2O production. The second turnover is attributed to the larger transfer coefficient (β) of HER than NH3 production. This work reveals how electrode potential regulate the selectivity of eNORR, which is also beneficial to understand the commonly increasing HER selectivity with the decrease of potential in some other electroreduction reactions such as CO2 reduction.

As we head towards the IoT (Internet of Things) era, protecting network infrastructures and information security has become increasingly crucial. In recent years, Anomaly-Based Network Intrusion Detection Systems (ANIDSs) have gained extensive attention for their capability of detecting novel attacks. However, most ANIDSs focus on packet header information and omit the valuable information in payloads, despite the fact that payload-based attacks have become ubiquitous. In this paper, we propose a novel intrusion detection system named TR-IDS, which takes advantage of both statistical features and payload features. Word embedding and text-convolutional neural network (Text-CNN) are applied to extract effective information from payloads. After that, the sophisticated random forest algorithm is performed on the combination of statistical features and payload features. Extensive experimental evaluations demonstrate the effectiveness of the proposed methods.

Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs). It is quite potential and promising to apply our model in the large-scale and real-world network environments.

Single-atom catalysts (SACs) have been studied widely in electrocatalysis towards renewable energy conversion. Herein, we present a general descriptor-based strategy for the rational design of SACs via density functional theory...

Recently, a bifunctional oxide–zeolite (OX-ZEO) catalyst was widely studied experimentally, which can selectively convert syngas to light olefins. The performance of OX-ZEO is exceptional, while the mechanism is controversial. In this work, we have first developed an algorithm based on graph theory to establish a complete reaction network for syngas conversion to methanol, ketene, and methane. Combined with density functional theory (DFT) calculations, the activity and selectivity of syngas conversion over zinc oxide (ZnO) are systematically studied by a reaction phase diagram. The key intermediate, ketene, is observed in experiments, which has been first confirmed theoretically in this work. The evolution of ZnO surfaces is found to be a key factor of diverse product selectivity. It is found that methanol production is more favored over the ZnO surfaces with a low oxygen vacancy concentration. As the oxygen vacancy increases, the main product evolves gradually from methanol to ketene and finally to methane. Accordingly, the overall reaction activity increases too. Our prediction from the reaction phase diagram is finally verified by microkinetic modeling.