Dalian Jiaotong University

Molybdenum disulfide (MoS2) is a promising nonprecious catalyst for the hydrogen evolution reaction (HER) that has been extensively studied due to its excellent performance, but the lack of understanding of the factors that impact its catalytic activity hinders further design and enhancement of MoS2-based electrocatalysts. Here, by using novel porous (holey) metallic 1T phase MoS2 nanosheets synthesized by a liquid-ammonia-assisted lithiation route, we systematically investigated the contributions of crystal structure (phase), edges, and sulfur vacancies (S-vacancies) to the catalytic activity toward HER from five representative MoS2 nanosheet samples, including 2H and 1T phase, porous 2H and 1T phase, and sulfur-compensated porous 2H phase. Superior HER catalytic activity was achieved in the porous 1T phase MoS2 nanosheets that have even more edges and S-vacancies than conventional 1T phase MoS2. A comparative study revealed that the phase serves as the key role in determining the HER performance, as 1T phase MoS2 always outperforms the corresponding 2H phase MoS2 samples, and that both edges and S-vacancies also contribute significantly to the catalytic activity in porous MoS2 samples. Then, using combined defect characterization techniques of electron spin resonance spectroscopy and positron annihilation lifetime spectroscopy to quantify the S-vacancies, the contributions of each factor were individually elucidated. This study presents new insights and opens up new avenues for designing electrocatalysts based on MoS2 or other layered materials with enhanced HER performance.

Hydrogen has been deemed as an ideal substitute fuel to fossil energy because of its renewability and the highest energy density among all chemical fuels. One of the most economical, ecofriendly, and high-performance ways of hydrogen production is electrochemical water splitting. Recently, 2D transition metal dichalcogenides (also known as 2D TMDs) showed their utilization potentiality as cost-effective hydrogen evolution reaction (HER) catalysts in water electrolysis. Herein, recent representative research efforts and systematic progress made in 2D TMDs are reviewed, and future opportunities and challenges are discussed. Furthermore, general methods of synthesizing 2D TMDs materials are introduced in detail and the advantages and disadvantages for some specific methods are provided. This explanation includes several important regulation strategies of creating more active sites, heteroatoms doping, phase engineering, construction of heterostructures, and synergistic modulation which are capable of optimizing the electrical conductivity, exposure to the catalytic active sites, and reaction energy barrier of the electrode material to boost the HER kinetics. In the last section, the current obstacles and future chances for the development of 2D TMDs electrocatalysts are proposed to provide insight into and valuable guidelines for fabricating effective HER electrocatalysts.

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

MoSe 2 is a promising earth‐abundant electrocatalyst for the hydrogen‐evolution reaction (HER), even though it has received much less attention among the layered dichalcogenide (MX 2 ) materials than MoS 2 so far. Here, a novel hydrothermal‐synthesis strategy is presented to achieve simultaneous and synergistic modulation of crystal phase and disorder in partially crystallized 1T‐MoSe 2 nanosheets to dramatically enhance their HER catalytic activity. Careful structural characterization and defect characterization using positron annihilation lifetime spectroscopy correlated with electrochemical measurements show that the formation of the 1T phase under a large excess of the NaBH 4 reductant during synthesis can effectively improve the intrinsic activity and conductivity, and the disordered structure from a lower reaction temperature can provide abundant unsaturated defects as active sites. Such synergistic effects lead to superior HER catalytic activity with an overpotential of 152 mV versus reversible hydrogen electrode (RHE) for the electrocatalytic current density of j = −10 mA cm −2 , and a Tafel slope of 52 mV dec −1 . This work paves a new pathway for improving the catalytic activity of MoSe 2 and generally MX 2 ‐based electrocatalysts via a synergistic modulation strategy.

Abstract. A Nationwide Nitrogen Deposition Monitoring Network (NNDMN) containing 43 monitoring sites was established in China to measure gaseous NH3, NO2, and HNO3 and particulate NH4+ and NO3− in air and/or precipitation from 2010 to 2014. Wet/bulk deposition fluxes of Nr species were collected by precipitation gauge method and measured by continuous-flow analyzer; dry deposition fluxes were estimated using airborne concentration measurements and inferential models. Our observations reveal large spatial variations of atmospheric Nr concentrations and dry and wet/bulk Nr deposition. On a national basis, the annual average concentrations (1.3–47.0 μg N m−3) and dry plus wet/bulk deposition fluxes (2.9–83.3 kg N ha−1 yr−1) of inorganic Nr species are ranked by land use as urban > rural > background sites and by regions as north China > southeast China > southwest China > northeast China > northwest China > Tibetan Plateau, reflecting the impact of anthropogenic Nr emission. Average dry and wet/bulk N deposition fluxes were 20.6 ± 11.2 (mean ± standard deviation) and 19.3 ± 9.2 kg N ha−1 yr−1 across China, with reduced N deposition dominating both dry and wet/bulk deposition. Our results suggest atmospheric dry N deposition is equally important to wet/bulk N deposition at the national scale. Therefore, both deposition forms should be included when considering the impacts of N deposition on environment and ecosystem health.

Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability. Then, the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDE-DBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance, and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods.

The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.

Highly efficient earth-abundant electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are of great importance for renewable energy conversion systems. Herein, guided by theoretical calculations, we demonstrate highly efficient water splitting in alkaline solution using quarternary mixed nickel iron phosphosulfide (Ni1–xFexPS3) nanosheets (NSs), even though neither NiPS3 nor FePS3 is a good HER (or OER) electrocatalyst. With tuned electronic structure and improved electrical conductivity induced by mixing appropriate amount of Fe into NiPS3, Ni0.9Fe0.1PS3 NSs display excellent HER activity (an overpotential of 72 mV vs reversible hydrogen electrode (RHE) at a geometric catalytic current density of −10 mA cm–2 and a Tafel slope of 73 mV dec–1), which is among the best HER catalysts under alkaline conditions. Ni0.9Fe0.1PS3 NSs also show a good apparent OER activity (an overpotential of 329 mV vs RHE at a catalytic current density of 20 mA cm–2 and a Tafel slope of 69 mV dec–1), although structural investigation indicates the formation of Ni(Fe)OOH and Ni(Fe)(OH)2 layers on the catalyst surface after OER reactions as likely the real active species. These mixed nickel iron phosphosulfide non-precious-metal electrocatalysts with enhanced intrinsic activity and long-term stability and durability should have great potential in overall water-splitting applications.

Reaction resonances, or transiently stabilized transition-state structures, have proven highly challenging to capture experimentally. Here, we used the highly sensitive H atom Rydberg tagging time-of-flight method to conduct a crossed molecular beam scattering study of the F + H2 --> HF + H reaction with full quantum-state resolution. Pronounced forward-scattered HF products in the v' = 2 vibrational state were clearly observed at a collision energy of 0.52 kcal/mol; this was attributed to both the ground and the first excited Feshbach resonances trapped in the peculiar HF(v' = 3)-H' vibrationally adiabatic potential, with substantial enhancement by constructive interference between the two resonances.

La2(MoO4)3 phosphors with various Eu3+ concentrations were prepared via a facile co-precipitation process. The crystal structure and morphology of the phosphors were characterized by means of XRD and field emission scanning electron microscope. The crystal unit cell parameters a, b, and c for the monoclinic phase La2(MoO4)3 were calculated to be 16.989, 11.927, and 16.086 Å, respectively. The average size of the phosphor particles was estimated to be around 88.5 nm. The Huang–Rhys factor was derived from the phonon sideband spectra to be 0.073. The self-generated quenching process of Eu3+ was explained based on Auzel’s model, and the intrinsic radiative transition lifetime for 5D0 level was confirmed to be 0.99 ms. A new approach for calculating the Judd–Ofelt parameters was developed, meanwhile the Judd–Ofelt parameters Ωλ (λ = 2, 4, 6) of Eu3+ in La2(MoO4)3 phosphors were confirmed to be 10.70 × 10−20, 1.07 × 10−20, and 0.56 × 10−20 cm2, respectively. Finally, the optimal doping concentration for achieving maximum emission intensity was confirmed to be 17 mol. % by analyzing the concentration quenching.

Network pharmacology, based on the theory of systems biology, is a new discipline that analyzes the biological network and screens out the nodes of particular interest, with the aim of designing poly-target drug molecule. It emphasizes maximizing drug efficacy and minimizing adverse effect via the multiple regulation of the signaling pathway. Coincidentally, almost all traditional Chinese medicine (TCM) and worldwide ethnomedicine exert therapeutic effect by targeting multiple molecules of the human body. In this overview, we offer a critique on the present perception of TCM and network pharmacology; illustrate the utility of network pharmacology in the study of single herb, medicine pair, and TCM formula; and summarize the recent progress of TCM-based drug discovery inspired by network pharmacology. Network pharmacology could be of great help in decreasing drug attrition rate and thus is essential in rational and cost-effective drug development. We also pinpoint the current TCM issues that could be tackled by the flexible combined use of network pharmacology and relevant disciplines.

Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated-frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing.

Broad Learning System (BLS) are widely used in many fields because of its strong feature extraction ability and high computational efficiency. However, the BLS is mainly used in supervised learning, which greatly limits the applicability of the BLS. And the obtained data is less labeled data, but is a large number of unlabeled data. Therefore, the BLS is extended based on the semi-supervised learning of manifold regularization framework to propose a semi-supervised broad learning system (SS-BLS). Firstly, the features are extracted from labeled and unlabeled data by building feature nodes and enhancement nodes. Then the manifold regularization framework is used to construct Laplacian matrix. Next, the feature nodes, enhancement nodes and Laplacian matrix are combined to construct the objective function, which is effectively solved by ridge regression in order to obtain the output coefficients. Finally, the validity of the SS-BLS is verified by three different complex data of G50C, MNIST, and NORB, respectively. The experiment result show that the SS-BLS can achieve higher classification accuracy for different complex data, takes on fast operation speed and strong generalization ability.

Traditional feature extraction methods are used to extract the features of signal to construct the fault feature matrix, which exists the complex structure, higher correlation, and redundancy. This will increase the complex fault classification and seriously affect the accuracy and efficiency of fault identification. In order to solve these problems, a new fault diagnosis (PABSFD) method based on the principal component analysis (PCA) and the broad learning system (BLS) is proposed for rotor system in this paper. In the proposed PABSFD method, the PCA with revealing the signal essence is used to reduce the dimension of the constructed feature matrix and decrease the linear feature correlation between data and eliminate the redundant attributes in order to obtain the low-dimensional feature matrix with retaining the essential features for the classification model. Then, the BLS with low time complexity and high classification accuracy is regarded as a classification model to realize the fault identification; it can efficiently accomplish the fault classification of rotor system. Finally, the actual vibration data of rotor system are selected to test and verify the effectiveness of the PABSFD method. The experimental results show that the PCA method can effectively eliminate the feature correlation and realize the dimension reduction of the feature matrix, the BLS can take on better adaptability, faster computation speed, and higher classification accuracy, and the PABSFD method can efficiently and accurately obtain the fault diagnosis results.

Performance prediction is significant to monitor the health status of rolling bearings, which can greatly reduce the loss caused by potential faults in the whole life cycle of rolling bearings. It is a very important part of Prognostic and Health Management (PHM). In this article, a new performance degradation prediction (HMEPEM) method based on high-order differential mathematical morphology gradient spectrum entropy (HOMMSE), phase space reconstruction, and extreme learning machine (ELM) is proposed to predict the performance degradation trend of rolling bearings. In the proposed HMEPEM method, the HOMMSE method is used to extract the initial features of performance degradation from the raw bearing vibration signals and divide working stages. Then the phase space reconstruction is used to further extract more useful features from the initial features of performance degradation in order to construct a feature matrix, which is input into the ELM in order to build the performance degradation prediction model for predicting the performance degradation trend of rolling bearings. The proposed HMEPEM method is validated on the performance degradation data of rolling bearings provided by the PRONOSTIA platform. The results show that the proposed HMEPEM method can efficiently track the evolution of degradation and predict the performance degradation trend of rolling bearings.

Abstract The design of efficient copper(Cu)‐based catalysts is critical for CO 2 electroreduction into multiple carbon products. However, most Cu‐based catalysts are favorable for ethylene production while selective production of ethanol with high Faradaic efficiency and current density still remains a great challenge. Herein, we design a carbon‐coated CuO x (CuO x @C) catalyst through one‐pot pyrolysis of Cu‐based metal‐organic framework (MOF), which exhibits high selectivity for CO 2 electroreduction to ethanol with Faradaic efficiency of 46 %. Impressively, the partial current density of ethanol reaches 166 mA cm −2 , which is higher than that of most reported catalysts. Operando Raman spectra indicate that the carbon coating can efficiently stabilize Cu + species under CO 2 electroreduction conditions, which promotes the C−C coupling step. Density functional theory (DFT) calculations reveal that the carbon layer can tune the key intermediate *HOCCH goes the hydrogenation pathway toward ethanol production.

Replacement of rare and precious metal catalysts with low-cost and earth-abundant ones is currently among the major goals of sustainable chemistry. Herein, we report the synthesis of S, N dual-doped graphene-like carbon nanosheets via a simple pyrolysis of a mixture of melamine and dibenzyl sulfide as efficient metal-free electrocatalysts for oxygen reduction reaction (ORR). The S, N dual-doped graphene-like carbon nanosheets show enhanced activity toward ORR as compared with mono-doped counterparts, and excellent durability in contrast to the conventional Pt/C electrocatalyst in both alkaline and acidic media. A high content of graphitic-N and pyridinic-N is necessary for ORR electrocatalysis in the graphene-like carbon nanosheets, but an appropriate amount of S atoms further contributes to the improvement of ORR activity. Superior ORR performance from the as-prepared S, N dual-doped graphene-like carbon nanosheets implies great promises in practical applications in energy devices.

Developing efficient and robust Earth-abundant hydrogen evolution reaction (HER) catalysts is crucial for the scalable and sustainable production of hydrogen. This study builds on the promising concept of oxide/metal nanocomposites, namely NiO/Ni, by incorporating another metal (in this case, Mo) into NiO to tune the adsorption energy of hydrogen (H). Theoretical and experimental approaches are combined to design Mo-NiO/Ni nanocomposites for efficient HER in alkaline media. The incorporation of Mo into NiO weakens the adsorption of H, favoring its transfer to nearby Ni active sites to accelerate the Volmer reaction, and also stabilizes the crystal structure of NiO. The Mo-NiO/Ni catalyst exhibits a 50 mV overpotential at a current density of −10 mA cm–2 with a Tafel slope of 86 mV dec–1, indicating that it is one of the most promising alkaline HER electrocatalysts reported to date. These findings suggest design principles for inexpensive, efficient, and high-performance Earth-abundant HER catalysts.

Background: Illumina second generation sequencing is now an efficient route for generating enormous sequence collections that represent expressed genes and quantitate expression level. Taxus is a world-wide endangered gymnosperm genus and forms an important anti-cancer medicinal resource, but the large and complex genomes of Taxus have hindered the development of genomic resources. The research of its tissue-specific transcriptome is absent. There is also no study concerning the association between the plant transcriptome and metabolome with respect to the plant tissue type.

A series of iron-modified Ce/TiO2 catalysts with different Fe/Ti molar ratios were prepared by an impregnation method and used for selective catalytic reaction (SCR) of NOx with NH3. The Fe–Ce/TiO2 catalyst with a Fe/Ti molar ratio of 0.2 had good low-temperature activity and sulfur-poisoning resistance compared with the Ce/TiO2 catalyst. The introduction of Fe could increase the amount of Ce3+ and chemisorbed oxygen species on the catalyst surface and thereafter generate more ionic NH4+ and in situ formed NO2, respectively. In addition, the dispersion of cerium oxide could be improved by the addition of iron, and no visible phase of iron oxide could be observed at low Fe/Ti molar ratios (≤0.2). All of these factors played significant roles in the enhanced catalytic activity, especially the low-temperature activity. Furthermore, mechanisms of the SCR reaction and the SO2 poisoning of the Fe(0.2)–Ce/TiO2 catalyst were studied using in situ diffuse reflectance infrared Fourier transform spectroscopy. Coordinated NH3 and ionic NH4+ species as well as adsorbed NO2 might be the key intermediates in the SCR reaction in the relatively low-temperature range. The formation of ammonium sulfate appeared to be the dominant cause for the catalyst deactivation in SO2-containing gases.