More than 20 000 MOFs have been reported to date, with different combinations of metal ions/centers and organic linkers, and they can be grown into various 3D, 2D, 1D and 0D morphologies. The flexibility in control over varying length scales from atomic scale up to bulk structure allows access to an almost endless variety of MOF-based and MOF-derived materials. Indeed, MOFs themselves have been studied as a class of useful functional materials. More remarkably, extensive research conducted in recent years has shown that MOFs are exceptionally good precursors for a large variety of nanohybrids as active materials in both electrocatalysis and energy storage. As they already contain both carbon and well-dispersed metal atoms, MOFs can be converted to conductive carbons decorated with active metal species and doping elements through appropriate pyrolysis. Due to the great diversity accessible in the composition, structure, and morphology of MOFs, several types of MOF-derived nanohybrids are now among the best performing materials both for electrocatalysts and electrodes in various energy conversion and storage devices. In addition to mesoporous nano-carbons, both doped and undoped, carbon-metal nanohybrids, and carbon-compound nanohybrids, there are several types of core@shell, encapsulated nanostructures, embedded nanosystems and heterostructures that have been developed from MOFs recently. They can be made in either free-standing forms, nano- or micro-powders, grown on appropriate conducting substrates, or assembled together with other active materials. During the MOF to active material conversion, other active species or precursors can be inserted into the MOF-derived nanostructures or assembled on surfaces, leading to uniquely new porous nanostructures. These MOF-derived active materials for electrocatalysis and energy storage are nanohybrids consisting of more than functional components that are purposely integrated together at desired length scales for much-improved performance. This article reviews the current status of these nanohybrids and concludes with a brief perspective on the future of MOF-derived functional materials.

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Implicit-explicit (IMEX) time stepping methods can efficiently solve differential equations with both stiff and nonstiff components. IMEX Runge-Kutta methods and IMEX linear multistep methods have been studied in the literature. In this paper we study new implicit-explicit methods of general linear type (IMEX-GLMs). We develop an order conditions theory for high stage order partitioned GLMs that share the same abscissae, and show that no additional coupling order conditions are needed. Consequently, GLMs offer an excellent framework for the construction of multi-method integration algorithms. Next, we propose a family of IMEX schemes based on diagonallyimplicit multi-stage integration methods and construct practical schemes of order three. Numerical results confirm the theoretical findings.

Periodic recreation of existing railway horizontal alignment geometry is needed for smoothing the deviations arising from train operations. It is important for calibrating track and rebuilding existing railways to ensure safety and comfort. Track calibration repairs the existing distorted track centerline to match the smoothed recreated alignment, which may differ considerably from the originally designed track centerline. Identifying the boundaries of all the geometric elements including tangents, circular curves, and transition curves is the key problem. Existing methods recreate the horizontal alignment semi‐automatically and can only generate a locally optimized solution while considering a few constraints. Based on the principle that the attributions of all the measured points to geometric elements should be consistent with the ranges of recreated geometric elements (i.e., for points‐alignment consistency), a method called swing iterations is proposed to reclassify point placements and identify all the tangents, circular curves, and transition curves simultaneously. In a swing iteration, the boundary of a geometric element segment repeatedly changes from left to right, then from the right to left, and finally stabilizes. Before the swing iterations, preliminary tangents and curves are identified based on the heading gradient (i.e., the rate of change of heading), and are set as initial values for the swing iterations. A genetic algorithm is developed to further refine the entire recreated alignment after the swing iterations. In the above processes, multiple constraints are handled. Applications demonstrate that this method can identify all horizontal geometric elements automatically and generate an optimized recreated alignment geometry for an existing railway while satisfying all the applicable constraints.