Metal-free boron- and carbon-based catalysts have shown both great fundamental and practical value in oxidative dehydrogenation (ODH) of light alkanes. In particular, boron-based catalysts show a ...superior selectivity toward olefins, excellent stability and atom-economy to valuable carbon-based products by minimizing CO
2
emission, which are highly promising in future industrialization. The carbonaceous catalysts also exhibited impressive behavior in the ODH of light alkanes helped along by surface oxygen-containing functional groups. This review surveyed and compared the preparation methods of the boron- and carbon-based catalysts and their characterization, their performance in the ODH of light alkanes, and the mechanistic issues of the ODH including the identification of the possible active sites and the exploration of the underlying mechanisms. We discussed different boron-based materials and established versatile methodologies for the investigation of active sites and reaction mechanisms. We also elaborated on the similarities and differences in catalytic and kinetic behaviors, and reaction mechanisms between boron- and carbon-based metal-free materials. A perspective of the potential issues of metal-free ODH catalytic systems in terms of their rational design and their synergy with reactor engineering was sketched.
Metal-free boron- and carbon-based catalysts for the oxidative dehydrogenation of light alkanes is reviewed from the preparation methods, characterization, catalytic performance and mechanistic issues.
•1. Oceanic water waves are actively studied. Taking into account the nonlinear and dispersive long gravity waves in two horizontal directions on the shallow water of an open sea or a wide channel of ...finite depth. 3. Investigating a generalized (2+1)-dimensional dispersive long-wave system. 4. With symbolic computation while with respect to the horizontal velocity and wave elevation above the undisturbed water surface. 5. working out two non-auto-Backlund transformations and two auto-Backlund transformations with solitons.
Oceanic water waves are actively studied. Hereby, taking into account the nonlinear and dispersive long gravity waves in two horizontal directions on the shallow water of an open sea or a wide channel of finite depth, we investigate a generalized (2+1)-dimensional dispersive long-wave system. With symbolic computation while with respect to the horizontal velocity and wave elevation above the undisturbed water surface, we work out two non-auto-Bäcklund transformations and two auto-Bäcklund transformations with solitons. All of our results are dependent on the constant coefficients in the original system.
Results Phys. 51, 106624 (2023) and 50, 106566 (2023) have recently made some outstanding contributions to the studies of certain Korteweg-de Vries (KdV)-type systems. Inspired by those ...contributions, around a noncharacteristic movable singular manifold, this Letter works out two sets of the auto-Bäcklund transformations for an enlarged three-coupled KdV system, along with some solitons. All of our results rely on the coefficients in that system.
Membership determination of open clusters in high-noise environments is still an open question. This paper aims to evaluate the effectiveness of the Gaussian mixture model (GMM) in segregating likely ...cluster members of open clusters in high-noise environments. We use the GMM method to segregate likely cluster members of four low Galactic latitude open clusters: NGC 2112, NGC 2477, NGC 7789, and Collinder 261, based on the high-precision astrometric data of the Gaia Data Release 2 (Gaia-DR2). The GMM method is used to calculate the membership probabilities of the stars in the field of each cluster; five astrometric parameters (positions, parallaxes, and proper motions) are taken into account. We quantitatively evaluate the goodness of the cluster-field segregation for each cluster, and find that the GMM method is effective for segregating likely cluster members of these clusters, even if these clusters suffer from heavy field star contamination. We estimate the distances and absolute proper motions of these clusters using reliable cluster members; our results suggest the existence of a significant zero-point offset in Gaia-DR2 parallaxes. NGC 2112, NGC 2477, NGC 7789, and Collinder 261 are found to have a mean distance of 〈 D 〉 = 1104 4 , 1437 2 , 2067 4 and 2802 21 pc, respectively. Mean proper motions of ( 〈 cos δ 〉 , 〈 δ 〉 ) = ( − 2.714 0.012 , 4.272 0.012 ) , (−2.449 0.006,0.876 0.006), (−0.919 0.004,−1.938 0.004), and (−6.348 0.006,−2.714 0.006) mas/yr are determined for NGC 2112, NGC 2477, NGC 7789, and Collinder 261, respectively.
In the Solar System, water and water waves are commonly seen: For the Earth, water is “at the core of sustainable development” and “at the heart of adaptation to climate change”; For the Enceladus, ...Cassini spacecraft discovers a possible global ocean of liquid water beneath an icy crust; For the Titan, Cassini spacecraft suggests an icy shell floating atop a global ocean. Shallow water waves near the ocean beaches or in the lakes can be described by the Boussinesq-Burgers-type equations. In this Letter, on the higher-order Boussinesq-Burgers system, symbolic computation helps us to go from the two-dimensional Bell polynomials to construct two non-auto-Bäcklund transformations and to proceed from the Painlevé-Bäcklund format to obtain four auto-Bäcklund transformations with some soliton solutions. All of our results are shown to be dependent on the constant coefficient in the system.
Cosmic plasmas are considered as the most abundant form of ordinary matter in the Universe while observations of the cosmic dust in different regions provide an insight into the Universe's recycling ...processes. For different types of the cosmic dusty plasmas, we hereby, with the symbolic computation and observational/experimental supports, study a (3+1)-dimensional generalized variable-coefficient Kadomtsev-Petviashvili-Burgers-type equation, which can describe the electron-acoustic, dust-acoustic, positron-acoustic, dust-magneto-acoustic, ion-acoustic, magneto-acoustic, ion, quantum-dust-ion-acoustic or dust-ion-acoustic waves in one of the cosmic/laboratory dusty plasmas. With respect to the fluctuation of the electron or ion density, or perturbation of the magnitude of the magnetic field, or electrostatic wave potential, or radial-direction component of the velocity of ions or dust particles, a set of the auto-Bäcklund transformations, several soliton families and a set of the similarity reductions are symbolically computed out, depending on the variable coefficients which represent the dispersion, nonlinearity, geometric effect, Burgers/dusty-fluid-viscosity dissipation and diffraction/transverse perturbation. Variable-coefficient constraints on the soliton solutions are presented. Our analytic results are in agreement with those dusty-plasma-experimentally reported. Future dusty-plasma experiments and observations might justify some other effects hereby offered.
Microporous SpectraCarb carbon cloth was treated using nitric acid to enhance negative surface charges of COO– in a neutral solution. This acid-treated carbon was further modified by ethylenediamine ...to attach −NH2 surface functional groups, resulting in positive surface charges of −NH3 + via pronation in a neutral solution. Through multiple characterizations, in comparison to pristine SpectraCarb carbon, amine-treated SpectraCarb carbon displays a decreased potential of zero charge but an increased point of zero charge, which is opposed to the effect obtained for acid-treated SpectraCarb carbon. An inverted capacitive deionization cell was constructed using amine-treated cathodes and acid-treated anodes, where the cathode is the negatively polarized electrode and the anode is the positively polarized electrode. Constant-voltage switching operation using NaCl solution showed that the salt removal capacity was approximately 5.3 mg g–1 at a maximum working voltage of 1.1/0 V, which is an expansion in both the salt capacity and potential window from previous i-CDI results demonstrated for carbon xerogel materials. This improved performance is accounted for by the enlarged cathodic working voltage window through ethylenediamine-derived functional groups, and the enhanced microporosity of the SpectraCarb electrodes for salt adsorption. These results expand the use of i-CDI for efficient desalination applications.
•Exoteric introduction of deep learning and its usage in bioinformatics.•Concrete and representative examples of using deep learning in bioinformatics.•Solutions and suggestions for handling common ...issues when using deep learning.•Thorough survey of the commonly used deep learning models for various data types.
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.
Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph ...network (GN) is employed to establish a correlation model between the crystal structure and formation enthalpies at the given database, and an optimization algorithm (OA) is used to accelerate the search for crystal structure with lowest formation enthalpy. The framework of the utilized approach (a database + a GN model + an optimization algorithm) is flexible. We implemented two benchmark databases, i.e., the open quantum materials database (OQMD) and Matbench (MatB), and three OAs, i.e., random searching (RAS), particle-swarm optimization (PSO) and Bayesian optimization (BO), that can predict crystal structures at a given number of atoms in a periodic cell. The comparative studies show that the GN model trained on MatB combined with BO, i.e., GN(MatB)-BO, exhibit the best performance for predicting crystal structures of 29 typical compounds with a computational cost three orders of magnitude less than that required for conventional approaches screening structures through density functional theory calculation. The flexible framework in combination with a materials database, a graph network, and an optimization algorithm may open new avenues for data-driven crystal structural predictions.
As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human ...activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen’s slope, Mann–Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.
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