Structure and Performance of Cu-Fe Bimodal Support for Higher Alcohol Syntheses

Copper-iron modified bimodal support (M) with different mass fractions of Cu and Fe elements were prepared by an ultrasonic impregnation method. The catalytic performance for higher alcohol syntheses (HAS) was investigated in a fixed-bed flow reactor. Several techniques, including N-2 physical adsorption, temperature-programmed reduction/desorption of hydrogen, (H-2-TPR/TPD) and X-ray diffraction (XRD) were used to characterize the catalysts. The results indicated that the bimodal pore support was formed by the addition of small-pore silica sol into the macroporous silica gel. Increased amounts of small pore silica sol caused a decrease in pore size in the bimodal carrier. An increase in the Fe/Cu molar ratio facilitated the dispersion of CuO, promoted the reduction of CuO and Fe2O3 on the surface layers, and enhanced the interaction between the copper and iron species as well as the bimodal support inside the large pores. The copper was well-dispersed on the catalyst and the amount of iron carbides formed was high in catalysts with a high Fe/Cu molar ratio. Increasing the Fe/Cu mass ratio promoted the catalytic activity and thus facilitated the synthesis of higher alcohols. When the Fe/Cu molar ratio was increased to 30/20, the CO conversion and the yield of higher alcohols increased to 46% and 0.21 g . mL(-1) . h(-1), respectively. At the same time, the mass ratio of C2+OH/CH3OH reached 1.96.</p

Complex-valued neural operator assisted soliton identification

The numerical determination of solitary states is an important topic for such research areas as Bose-Einstein condensates, nonlinear optics, plasma physics, etc. In this paper, we propose a data-driven approach for identifying solitons based on dynamical solutions of real-time differential equations. Our approach combines a machine-learning architecture called the complex-valued neural operator (CNO) with an energy-restricted gradient optimization. The former serves as a generalization of the traditional neural operator to the complex domain, and constructs a smooth mapping between the initial and final states; the latter facilitates the search for solitons by constraining the energy space. We concretely demonstrate this approach on the quasi-one-dimensional Bose-Einstein condensate with homogeneous and inhomogeneous nonlinearities. Our work offers a new idea for data-driven effective modeling and studies of solitary waves in nonlinear physical systems.Comment: 9 pages, 5 figure

Node-line Dirac semimetal manipulated by Kondo mechanism in nonsymmorphic CePt2_2Si2_2

Dirac node lines (DNLs) are characterized by Dirac-type linear crossings between valence and conduction bands along one-dimensional node lines in the Brillouin zone (BZ). Spin-orbit coupling (SOC) usually shifts the degeneracy at the crossings thus destroys DNLs, and so far the reported DNLs in a few materials are non-interacting type, making the search for robust interacting DNLs in real materials appealing. Here, via first-principle calculations, we reveal that Kondo interaction together with nonsymmorphic lattice symmetries can drive a robust interacting DNLs in a Kondo semimetal CePt_2Si_2, and the feature of DNLs can be significantly manipulated by Kondo behavior in different temperature regions. Based on the density function theory combining dynamical mean-field theory (DFT+DMFT), we predict a transition to Kondo-coherent state at coherent temperature T_coh= 80 K upon cooling, verified by temperature dependence of Ce-4f self-energy, Kondo resonance peak, magnetic susceptibility and momentum-resolved spectral. Below T_coh, well-resolved narrow heavy-fermion bands emerge near the Fermi level, constructing clearly visualized interacting DNLs locating at the BZ boundary, in which the Dirac fermions have strongly enhanced effective mass and reduced velocity. In contrast, above a crossover temperature T_KS =600 K, the destruction of local Kondo screening drives non-interacting DNLs which are comprised by light conduction electrons at the same location. These DNLs are protected by lattice nonsymmorphic symmetries thus robust under intrinsic strong SOC. Our proposal of DNLs which can be significantly manipulated according to Kondo behavior provides an unique realization of interacting Dirac semimetals in real strongly correlated materials, and serves as a convenient platform to investigate the effect of electronic correlations on topological materials.Comment: 9 pages, 9 figure

Assessing environmental fate of β-HCH in Asian soil and association with environmental factors

Chinese Gridded Pesticide Emission and Residue Model was applied to simulate long-term environmental fate of beta-HCH in Asia spanning 1948-2009. The model captured well the spatiotemporal variation of beta-HCH soil concentrations across the model domain. beta-HCH use in different areas within the model domain was simulated respectively to assess the influence of the different sources of beta-HCH on its environment fate. A mass center of soil residue (MCSR) was introduced and used to explore environmental factors contributing to the spatiotemporal variation of beta-HCH soil residue. Results demonstrate that the primary emission dominates beta-HCH soil residues during the use of this pesticide. After phase-out of the pesticide in 1999, the change in beta-HCH soil residues has been associated with the Asian summer monsoon, featured by northward displacement of the MCSR. The displacement from several major sources in China and northeastern Asia shows a downward trend at a 95% confidence level, largely caused by environmental degradation and northward delivery of beta-HCH under cold condition in northern area. The MCSRs away from the India and southern and southeastern Asia sources show a rapid northward displacement at a 99% confidence level, featuring the cold trapping effect of the Tibetan Plateau.Chinese Gridded Pesticide Emission and Residue Model was applied to simulate long-term environmental fate of beta-HCH in Asia spanning 1948-2009. The model captured well the spatiotemporal variation of beta-HCH soil concentrations across the model domain. beta-HCH use in different areas within the model domain was simulated respectively to assess the influence of the different sources of beta-HCH on its environment fate. A mass center of soil residue (MCSR) was introduced and used to explore environmental factors contributing to the spatiotemporal variation of beta-HCH soil residue. Results demonstrate that the primary emission dominates beta-HCH soil residues during the use of this pesticide. After phase-out of the pesticide in 1999, the change in beta-HCH soil residues has been associated with the Asian summer monsoon, featured by northward displacement of the MCSR. The displacement from several major sources in China and northeastern Asia shows a downward trend at a 95% confidence level, largely caused by environmental degradation and northward delivery of beta-HCH under cold condition in northern area. The MCSRs away from the India and southern and southeastern Asia sources show a rapid northward displacement at a 99% confidence level, featuring the cold trapping effect of the Tibetan Plateau

Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations

Navier-Stokes equations are significant partial differential equations that describe the motion of fluids such as liquids and air. Due to the importance of Navier-Stokes equations, the development on efficient numerical schemes is important for both science and engineer. Recently, with the development of AI techniques, several approaches have been designed to integrate deep neural networks in simulating and inferring the fluid dynamics governed by incompressible Navier-Stokes equations, which can accelerate the simulation or inferring process in a mesh-free and differentiable way. In this paper, we point out that the capability of existing deep Navier-Stokes informed methods is limited to handle non-smooth or fractional equations, which are two critical situations in reality. To this end, we propose the \emph{Deep Random Vortex Method} (DRVM), which combines the neural network with a random vortex dynamics system equivalent to the Navier-Stokes equation. Specifically, the random vortex dynamics motivates a Monte Carlo based loss function for training the neural network, which avoids the calculation of derivatives through auto-differentiation. Therefore, DRVM not only can efficiently solve Navier-Stokes equations involving rough path, non-differentiable initial conditions and fractional operators, but also inherits the mesh-free and differentiable benefits of the deep-learning-based solver. We conduct experiments on the Cauchy problem, parametric solver learning, and the inverse problem of both 2-d and 3-d incompressible Navier-Stokes equations. The proposed method achieves accurate results for simulation and inference of Navier-Stokes equations. Especially for the cases that include singular initial conditions, DRVM significantly outperforms existing PINN method