Organic photodetectors (OPDs) for near infrared (NIR) light detection represents cutting‐edge technology for optical communication, environmental monitoring, biomedical imaging, and sensing. Herein, a series of self‐powered OPDs with high detectivity are constructed by the sequential deposition (SD) method. The dark currents (Jd) of SD devices are effectively reduced in comparison to blend casting (BC) ones due to the vertical phase segregation structure. Impressively, the Jd values of SD devices based on D18 and Y6 system is reduced to be 2.1 × 10−11 A cm−2 at 0 V, which is two orders of magnitude lower than those of the BC devices. The D* value of the SD device is superior to that of BC device under different bias voltages (0, −0.5, −1.0, and −2.0 V) due to the reduction of dark current, which originates from the fine vertical phase separation structure of the SD device. The mechanism studies shows that the vertical phase segregation structure can effectively suppress the unfavorable charge injection, thus reducing the dark current. Also, the surface energy is proven to play a key role in the photocurrent stability. In addition, the flexible OPDs demonstrate excellent performance in photoplethysmography test.

The large energy loss (E loss ) is one of the main obstacles to further improve the photovoltaic performance of organic solar cells (OSCs), which is closely related to the charge transfer (CT) state. Herein, ternary donor alloy strategy is used to precisely tune the energy of CT state (E CT ) and thus the E loss for boosting the efficiency of OSCs. The elevated E CT in the ternary OSCs reduce the energy loss for charge generation (𝚫E CT ), and promote the hybridization between localized excitation state and CT state to reduce the nonradiative energy loss (𝚫E nonrad ). Together with the optimal morphology, the ternary OSCs afford an impressive power conversion efficiency of 19.22% with a significantly improved open-circuit voltage (V oc ) of 0.910 V without sacrificing short-cicuit density (J sc ) and fill factor (FF) in comparison to the binary ones. This contribution reveals that precisely tuning the E CT via donor alloy strategy is an efficient way to minimize E loss and improve the photovoltaic performance of OSCs.

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The original BP neural network has some disadvantages, such as slow convergence speed, low precision, which is easy to fall into local minimum value. So this paper proposes an improved particle swarm optimization (PSO) algorithm to optimize BP neural network. In this new algorithm, PSO uses improved adaptive acceleration factor and improved adaptive inertia weight to improve the initial weight value and threshold value of BP neural network. And we give the detailed improved process. At the end, simulation results show that the new algorithm can improve convergence rate and precision of prediction of BP neural network, which reduces the error of prediction. At the end, we use multimedia evaluation model to verify the new method's performance.