This article describes the performance of organic photovoltaic (OPV) devices, incorporating three different polymer/fullerene derivative blends, under low‐level lighting conditions. The devices exhibit much higher power conversion efficiencies (PCEs) under indoor lighting conditions than they do under sunlight. The best‐performing device is capable of delivering a power output of 22.57 μW cm−2, corresponding to a PCE of 13.76%, under illumination with indoor lighting conditions at 500 lux. Increasing the open‐circuit voltage (Voc) of the OPV devices is the most critical factor for achieving high device performance for low‐power indoor applications. Therefore, the device power output will be maximized if we could obtain a larger energy difference between the highest occupied molecular orbital of the polymer donor and the lowest unoccupied molecular orbital of the electron acceptor, thereby ensuring a high value of Voc.

Atomically thin two-dimensional (2D) transition metal dichalcogenides have also attracted immense interest because they exhibit appealing electronic, optical and mechanical properties. In this work, we prepared gold nanoparticle-decorated molybdenum sulfide (AuNP@MoS2) through a simple spontaneous redox reaction. Transmission electron microscopy, UV-Vis spectroscopy, and Raman spectroscopy were used to characterize the properties of the AuNP@MoS2 nanomaterials. Then we employed such nanocomposites as the cathode buffer layers of organic photovoltaic devices (OPVs) to trigger surface plasmonic resonance, leading to noticeable enhancements in overall device efficiencies. We attribute the primary origin of the improvement in device performance to local field enhancement induced by the effects of localized surface plasmonic resonance. Our results suggest that the metal nanoparticle-decorated two-dimensional materials appear to have great potential for use in high-performance OPVs.

Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value.

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Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.

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