Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Consciously Constructing Heterojunction or Direct Z-Scheme Photocatalysts by Regulating Electron Flow Direction

Abstract: Heterojunction and direct Z-scheme nanostructures are two typical representatives of an efficient photocatalyst, which is composed of two semiconductors. However, it is a great challenge to construct each of them on purpose. The photodeposition technique can be a potentially powerful tool to regulate the electron flow direction for constructing these nanostructures. In this report, CdS nanoparticles were deposited on the g-C3N4 nanosheets by photodeposition and chemical deposition methods for comparison. In the photodeposition case, PL and charge flow tracking demonstrate that a type II heterojunction is constructed because CdS is selectively deposited at the electron transfer site of g-C3N4, which leads to the photoexcited electron from g–C3N4 tending to transfer to CdS in the composites. In the latter, the CdS is randomly deposited onto the g-C3N4 nanosheets through chemical deposition. There is no preferred site for deposition or charge transfer in the composite. The results illustrate that the electro...

MOF Template‐Directed Fabrication of Hierarchically Structured Electrocatalysts for Efficient Oxygen Evolution Reaction

Abstract: The ever-increasing demand for clean and renewable power sources has sparked intensive research on water splitting to produce hydrogen, in which the exploration of electrocatalysts is the central issue. Herein, a new strategy, metal–organic framework template-directed fabrication of hierarchically structured Co3O4@X (X = Co3O4, CoS, C, and CoP) electrocatalysts for efficient oxygen evolution reaction (OER) is developed, where Co3O4@X are derived from cobalt carbonatehydroxide@zeolitic-imidazolate-framework-67 (CCH@ZIF-67). Unique hierarchical structure and synergistic effect of resulting catalysts endow abundant exposed active sites, facile ion diffusion path, and improved conductivity, being favorable for improving catalytic activity of them. Consequently, these derivatives Co3O4@X reveal highly efficient electrocatalytic performance with long-term durability for the OER, much superior to previously reported cobalt-based catalysts as well as the Ir/C catalyst. Particularly, Co3O4@CoP exhibits the highest electrocatalytic capability with the lower overpotential of 238 mV at the current density of 10 mA cm−2. Furthermore, Co3O4@X can also efficiently catalyze other small molecules through electro-oxidation reaction (e.g., glycerol, methanol, or ethanol). It is expected that the strategy presented here can be extended to the fabrication of other composite electrode materials with hierarchical structures for more efficient water splitting.

Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

Abstract: Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.

Constructing NiCo/Fe3O4 Heteroparticles within MOF-74 for Efficient Oxygen Evolution Reactions

Abstract: Metal–organic frameworks (MOF) have recently emerged as versatile precursors to fabricate functional MOF derivatives for oxygen evolution reactions (OER). Herein, we developed a controlled partial pyrolysis strategy to construct robust NiCo/Fe3O4 heteroparticles within MOF-74 for efficient OER using trimetallic NiCoFe-MOF-74 as precursor. The partial pyrolysis method preserves the framework structure of MOF for effective substrates diffusion while producing highly active nanoparticles. The as-prepared NiCo/Fe3O4/MOF-74 delivered remarkably stable OER current with an overpotential as low as 238 mV at 10.0 mA cm–2 and an Tafel slop of 29 mV/dec, outperforming those of pristine NiCoFe-MOF-74, totally decomposed MOF derivatives, and most reported non-noble metal based electrocatalysts. The key for the formation of NiCo/Fe3O4/MOF-74 nanostructures is that the metals can be decomposed from NiCoFe-MOF-74 in the order of Ni, Co, and Fe under controlled heat treatment. Density functional theory calculations reveal...