The B-interstitial C2N layer can be utilized as a novel metal-free electrocatalyst with high efficiency and selectivity for the NRR due to its low limiting potential and significant suppressing effect on the HER.
The nitrogen reduction reaction (NRR) under ambient conditions using renewable energy is a green and sustainable strategy for the synthesis of NH3, which is one of the most important chemicals and carbon-free carriers. Thus, the search for low-cost, highly efficient, and stable NRR electrocatalysts is critical to achieve this goal. Herein, using comprehensive density functional theory (DFT) computations, we design a new class of NRR electrocatalysts based on a single transition metal (TM) atom supported on the experimentally feasible two-dimensional C2N monolayer (TM@C2N). Based on the computed free energies of each elementary pathway, Mo@C2N is predicted to exhibit the best catalytic activity among the TM@C2N, in which the proton-coupled electron transfer of the NH2* species to NH3(g) is the potential-determining step. Especially, the computed onset potential of the NRR on Mo@C2N is -0.17 V, which is even lower than that for the well-established stepped Ru(0001) surface (-0.43 V). Furthermore, the NRR catalytic performance of these TM@C2N can be well explained by their adsorption strength with N2H* species. Our findings open a new avenue for optimizing the TM catalytic performance for the NRR with the lowest number of metal atoms on porous low-dimensional materials.
Reliability prediction in power electronic converters is of paramount importance for converter manufacturers and operators. Conventional approaches employ generic data provided in handbooks for random chance failure probability prediction within useful lifetime. However, the wearout failures affect the long-term performance of the converters. Therefore, this paper proposes a comprehensive approach for estimating the converter reliability within useful lifetime and wear-out period. Moreover, this paper proposes a wear-out failure prediction approach based on a structural reliability concept. The proposed approach can quickly predict the converter wear-out behavior unlike conventional Monte Carlo based techniques. Hence, it facilitates reliability modeling and evaluation in large-scale power electronic based power systems with huge number of components. The proposed comprehensive failure function over the useful lifetime and wear-out phase can be used for optimal design and manufacturing by identifying the failure prone components and end-of-life prediction. Moreover, the proposed reliability model can be used for optimal decisionmaking in design, planning, operation and maintenance of modern power electronic based power systems. The proposed methodology is exemplified for a photovoltaic inverter by predicting its failure characteristics.
For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K 3 ) to O(K 2 ), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.
Searching for low-cost, efficient, and stable electrocatalysts for CO electroreduction (COER) reactions is highly desirable for the reduction of CO emission and its conversion into useful products, but remains a great challenge. In this work, single transition metal atoms supported on porphyrin-like graphene catalysts, i.e., TMN/graphene, acting as electrocatalysts for CO reduction were explored by means of comprehensive density functional theory (DFT) computations. Our results revealed that these anchored TM atoms possess high stability due to their strong hybridization with the unsaturated N atoms of the substrate and function as the active sites. On the basis of the calculated adsorption strength of COER intermediates, we have identified that single Co, Rh, and Ir atoms exhibit superior catalytic activity towards CO reduction. In particular, CHOH is the preferred product of COER on the CoN/graphene catalyst with an overpotential of 0.59 V, while the RhN/graphene and IrN/graphene catalysts prefer to reduce CO to CHO with an overpotential of 0.35 and 0.29 V, respectively. Our work may open a new avenue for the development of catalytic materials with high efficiency for CO electroreduction.
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