Achieving superior hydrogen storage properties via in-situ formed nanostructures: A high-capacity Mg–Ni alloy with La microalloying

Mg-based hydride is a promising hydrogen storage material, but its capacity is hindered by the kinetic properties. In this study, Mg–Mg₂Ni–LaH_x nanocomposite is formed from the H-induced decomposition of Mg₉₈Ni_1·67La_0.33 alloy. The hydrogen capacity of 7.19 wt % is reached at 325 °C under 3 MPa H₂, attributed to the ultrahigh hydrogenation capacity in Stage I. The hydrogen capacity of 5.59 wt % is achieved at 175 °C under 1 MPa H₂. The apparent activation energies for hydrogen absorption and desorption are calculated as 57.99 and 107.26 kJ/mol, which are owing to the modified microstructure with LaH_x and Mg₂Ni nanophases embedding in eutectic, and tubular nanostructure adjacent to eutectic. The LaH_2.49 nanophase can catalyze H₂ molecules to dissociate and H atoms to permeate due to its stronger affinity with H atoms. The interfaces of these nanophases provide preferential nucleation sites and alleviate the “blocking effect” together with tubular nanostructure by providing H atoms diffusion paths after the impingement of MgH₂ colonies. Therefore, the superior hydrogenation properties are achieved because of the rapid absorption process of Stage I. The efficient synthesis of nano-catalysts and corresponding mechanisms for improving hydrogen storage properties have important reference to related researches.     

Catalytic behavior in CH4 decomposition of catalysts derived from red mud: Impact of residual Na2O

Catalyst samples for CH₄ decomposition were prepared from red mud (RM) by an acid-leaching neutralization precipitation approach. Water-washing the resultant precipitates multiple times, followed by drying at 105 °C and calcination at 500 °C, resulted in a threshold of residual Na₂O, equivalent to 96% Na₂O removal. Drying the precipitate at a higher temperature of 200 °C, followed by repeated water washing, provided a deeper Na₂O removal of 99% and made the resultant samples more active for the targeted reaction. Subsequently, four catalyst samples with a simulated red mud composition and NaOH contents from 0 to 0.3 wt% were prepared and the catalytic test results revealed that the Na₂O remaining in the RM-derived catalysts did not only inhibit their activation in CH₄ but also lower their maximal activities for CH₄ decomposition. Finally, two catalysts with the same simulated red mud composition and their Na impregnated respectively on Fe₂O₃ and a mixture support of Al₂O₃-SiO₂-TiO₂ were prepared and tested to explore the effect of Na distribution on the activation behavior of RM-derived catalysts for CH₄ decomposition. The activity testing results showed that it was the Na residual dispersed on iron oxides in the RM-derived samples to significantly inhibit the activation of CH₄ decomposition.     

Incorporating microgrids coupling with utilization of flexible switching to enhance self-healing ability of electric distribution systems

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.     

Preparation of high-activity coal char-based catalysts from high metals containing coal gangue and lignite for catalytic decomposition of biomass tar

The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications.     

液相沉淀-煅烧法制备大粒径球形四氧化三钴

以硫酸钴为原料,碳酸氢铵为沉淀剂,采用液相沉淀法合成了大粒径球形碳酸钴,考察了不同晶种量、pH和硫酸钴溶液流量对碳酸钴形貌、粒度分布、振实密度和硫元素质量分数的影响,并探究了碳酸钴的生长机理。通过分步煅烧,并设置不同升温时间使碳酸钴热分解,得出优化四氧化三钴理化指标的煅烧条件。结果表明,当晶种量为2 kg,pH在7.2～7.5,硫酸钴溶液流量为500 mL/h时,采用分段式热分解碳酸钴,各温区按统一时间(60 min)升温,所得四氧化三钴形貌为球形,中值粒径为16.52μm,振实密度达2.26 g/cm3。     

Morphology-dependent spin Seebeck effect in yttrium iron garnet thin films prepared by metal-organic decomposition

In this study, we examined the dependence of surface morphology and spin Seebeck effect (SSE) voltages on the poly[vinylpyrrolidone] (PVP) concentration in polycrystalline Y₃Fe₅O₁₂ (YIG) ultrathin films on a silicon substrate synthesized by metal-organic decomposition followed by a crystallization process. During fabrication, PVP concentrations of 0.5–2 g were used while all other conditions remained fixed. Atomic force microscopy and grazing incidence X-ray diffraction (XRD) measurements revealed a strong dependence of crystallinity and sample morphology on PVP concentration. The 1-g PVP sample had the smoothest surface, with a root mean square roughness of 0.2 nm, as well as superior bulk uniformity with respect to the shape and intensity of XRD reflection peaks. This was confirmed by scanning electron microscopy measurements of a cross-section of the sample that revealed a uniform film without pores. SSE measurements were performed to obtain the output SSE voltages (V_SSE) of all samples, to which a platinum layer was added as a spin-detection layer. Repeatedly, the 1-g PVP sample had the best performance, demonstrating the importance of film crystallinity and morphology in the spin-to-charge conversion efficiency of YIG films.     

Strong tribocatalysis of strontium titanate nanofibers through harvesting friction energy for dye decomposition

Friction is a common clean energy and can be harvested and converted into electricity energy via triboelectricity, which can electrochemically drive dye decomposition in theory. In this work, the tribocatalytic Rhodamine B dye decomposition has been experimentally realized in strontium titanate (SrTiO₃) nanofibers, which are synthesized via a hydrothermal method. In the tribocatalytic dye decomposition process, the friction is exerted in the interface between catalyst surface and a polytetrafluoroethylene (PTFE) Teflon rod setup with the different stirring speed. The RhB dye decomposition ratios of SrTiO₃ nanofibers at these stirring speeds of 200 rpm, 400 rpm, 600 rpm, and 800 rpm are respectively 24.2%, 51.8%, 73.9% and 88.6%, yielding to these reaction rate constants of ～0.0112 h^?1, ～0.0260 h^?1, ～0.0562 h^?1 and ～0.0877 h^?1. The main active species, which play an important role in tribocatalytic process, are the superoxide radicals and holes on basis of the active species quenching experiment results. The excellent tribocatalysis activity makes SrTiO₃ nanofibers potential for application in dye wastewater treatment through utilizing the environmental friction energy.     

Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.