We report a new method to promote the conductivities of metal-organic frameworks (MOFs) by 5 to 7 magnitudes, thus their potential in electrochemical applications can be fully revealed. This method combines the polarity and porosity advantages of MOFs with the conductive feature of conductive polymers, in this case, polypyrrole (ppy), to construct ppy-MOF compartments for the confinement of sulfur in Li-S batteries. The performances of these ppy-S-in-MOF electrodes exceed those of their MOF and ppy counterparts, especially at high charge-discharge rates. For the first time, the critical role of ion diffusion to the high rate performance was elucidated by comparing ppy-MOF compartments with different pore geometries. The ppy-S-in-PCN-224 electrode with cross-linked pores and tunnels stood out, with a high capacity of 670 and 440 mAh g at 10.0 C after 200 and 1000 cycles, respectively, representing a new benchmark for long-cycle performance at high rate in Li-S batteries.

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Cascade processes are gaining momentum in heterogeneous catalysis. The combination of several catalytic solids within one reactor has shown great promise for the one-step valorization of C1-feedstocks. The combination of metal-based catalysts and zeolites in the gas phase hydrogenation of CO2 leads to a large degree of product selectivity control, defined mainly by zeolites. However, a great deal of mechanistic understanding remains unclear: metal-based catalysts usually lead to complex product compositions that may result in unexpected zeolite reactivity. Here we present an in-depth multivariate analysis of the chemistry involved in eight different zeolite topologies when combined with a highly active Fe-based catalyst in the hydrogenation of CO2 to olefins, aromatics, and paraffins. Solid-state NMR spectroscopy and computational analysis demonstrate that the hybrid nature of the active zeolite catalyst and its preferred CO2-derived reaction intermediates (CO/ester/ketone/hydrocarbons, i.e., inorganic-organic supramolecular reactive centers), along with 10 MR-zeolite topology, act as descriptors governing the ultimate product selectivity.

Discoveries of the accurate spatial arrangement of active sites in biological systems and cooperation between them for high catalytic efficiency are two major events in biology. However, precise tuning of these aspects is largely missing in the design of artificial catalysts. Here, a series of metal–organic frameworks (MOFs) were used, not only to overcome the limit of distance between active sites in bio‐systems, but also to unveil the critical role of this distance for efficient catalysis. A linear correlation was established between photocatalytic activity and the reciprocal of inter active‐site distance; a smaller distance led to higher activity. Vacancies created at selected crystallographic positions of MOFs promoted their photocatalytic efficiency. MOF‐525‐J33 with 15.6 Å inter active‐site distance and 33 % vacancies exhibited unprecedented high turnover frequency of 29.5 h−1 in visible‐light‐driven acceptorless dehydrogenation of tetrahydroquinoline at room temperature.

This article assesses the energy management of reconfigurable residential smart hybrid AC/DC microgrids considering the combined heat and power (CHP) loads as well as the electric vehicles charging/discharging behaviors. A holistic model is developed for the proton exchange membrane fuel cell to retrieve the unwanted thermal energy generated at the operation time. The proposed model makes use of the unoccupied capacity of the fuel cell for producing/storing hydrogen for the later usage and increasing its efficiency. A stochastic framework is designed using point estimate method (PEM) to capture the uncertainties of the photovoltaic and wind turbine forecast error, power company price, the operating temperature of the proton exchange membrane fuel cell, the price for natural gas, price for selling hydrogen, and the pressure of the H2 and O2 in the fuel cell stack. The PEM approach has shown superior advantages in terms of accuracy and running time. Considering the complex and nonlinear structure of the proposed framework, a proficient optimization technique based on the teacher learning algorithm (TLA) is devised. A two-phase modification method is proposed to increase the algorithm variety and help its convergence characteristics. The performance of the proposed algorithm is compared with the TLA, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). For enhancing the security of the energy and data transaction within the system, a directed acyclic graph (DAG)-based security framework is introduced to guarantee the performance of the system against the subversive accesses. By using this scheme, the essential data of the units are recorded and secured in the form of public, private and transaction blockchains. The economic characteristics of the proposed method are assessed on a residential hybrid AC-DC microgrid test system.