Using plasma-enhanced chemical vapor deposition (PECVD) to directly grow graphene nanowalls (GNWs) on silicon to preparate the solar cells is compatible with current industrial production. However, many defects in the GNWs hinder improvement of the power conversion efficiency (PCE) of solar cells. In this work, we found that the defects in GNWs can be reduced under the condition of keeping the appropriate sheet resistance of GNWs by simultaneously reducing the growth temperature and increasing the growth time. Then, a PCE of 3.83% was achieved by minimizing the defects in the GNWs under the condition of ensuring adequate coverage of GNWs on bare planar silicon. The defects in GNWs were further reduced by adding a poly(3,4-ethylenedioxythiophene) (PEDOT):Nafion passivation coating, and the PCE was significantly improved to10.55%. Our work provides an innovative path and a simple approach to minimize the defects in graphene grown directly on silicon for high-efficiency solar cells.
Layouts of bus networks in cities are always irrational currently, transport service frequencies also need to be optimized according to the real network layouts, operation conditions and travel experience of passengers, so it is essential to optimize bus transit network layouts and transport service frequencies systematically. Different stakeholders are involved in the optimization of urban bus transit network layouts like the government, operators and passengers, whose interests are always contradictory. In order to optimize transit network layout and service frequencies from the view point of operators and utilizers, this research constructs a multi-objective model and proposes a solution algorithm. The proposed multi-objective model is established from the perspective of operators with the goal of minimizing total operating costs for one day, and from the perspective of the utilizers to minimize the total travel time, respectively. Also with the application of electric bus in cities, buses in this research are electric buses all for green travel. Moreover, a solution algorithm is proposed in this research to solve the proposed multi-objective model with simulated annealing algorithm and genetic algorithm. Simulated annealing algorithm is used as the main framework of the solution algorithm from the perspective of operators to minimize operating costs, while genetic algorithm is used as the subroutine of simulated annealing algorithm to optimize total travel time. Verification of the proposed model and the solution algorithm is based on an intuitive network. The application results of a numerical experiment verified that the proposed optimization model and the solution algorithm are able to optimize the network layout and service frequencies at the same time.
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