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Indoor photovoltaics are receiving tremendous attention due to the continuous development of the Internet of Things (IoT). Here we report a triple anion (TA) perovskite CH3NH3PbI2.6(BrCl)0.2 with a tailored bandgap suitable for maximizing indoor light harvesting compared to methyl ammonium lead iodide CH3NH3PbI3. The best-performing TA perovskite indoor-photovoltaic device achieved a steady-state power conversion efficiency (PCE) of 25.1% with an output power density of ~ 75 microW/cm2 under 1000 lux indoor illumination (0.3 mW/cm2 irradiance). This PCE is almost 40% higher than that of equivalent CH3NH3PbI3-based devices (PCE of 17.9%). Longer carrier lifetime, reduced density of trap states and improved crystalline quality were achieved by the triple anion alloying method. The decisive role of chlorine (Cl) in the better performance of TA-based indoor photovoltaic devices was further investigated by successively reducing the Cl content and correlating it with the corresponding photovoltaic device performance. Replacing the commonly used hole transporting layer of Spiro-MeOTAD with undoped P3HT was found to significantly reduce the current-voltage hysteresis under indoor lighting conditions. A graphene-coated textile fiber-based temperature sensor was successfully powered by the triple anion perovskite indoor photovoltaic devices. The results from the present study demonstrate a novel route to maximize the PCE of halide perovskite indoor photovoltaic devices and their potential for application in the IoT industry., Comment: 27 pages, 5 figures

This paper proposes an effective and novel multiagent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The new joint reward and internal rewards mechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes., Comment: 17 pages, 15 figures, Accepted by IEEE Transactions on Communications

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users., Comment: 14 pages, 10 figures, Accepted by IEEE Transactions on Wireless Communications

We propose a learning-based scheme to investigate the dynamic multi-channel access (DMCA) problem in the fifth generation (5G) and beyond networks with fast time-varying channels wherein the channel parameters are unknown. The proposed learning-based scheme can maintain near-optimal performance for a long time, even in the sharp changing channels. This scheme greatly reduces processing delay, and effectively alleviates the error due to decision lag, which is cased by the non-immediacy of the information acquisition and processing. We first propose a psychology-based personalized quality of service model after introducing the network model with unknown channel parameters and the streaming model. Then, two access criteria are presented for the living streaming model and the buffered streaming model. Their corresponding optimization problems are also formulated. The optimization problems are solved by learning-based DMCA scheme, which combines the recurrent neural network with deep reinforcement learning. In the learning-based DMCA scheme, the agent mainly invokes the proposed prediction-based deep deterministic policy gradient algorithm as the learning algorithm. As a novel technical paradigm, our scheme has strong universality, since it can be easily extended to solve other problems in wireless communications. The real channel data-based simulation results validate that the performance of the learning-based scheme approaches that derived from the exhaustive search when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision at every few time-slots., Comment: 16 pages, 20 figures

Phenylenediamine (PDA) was chosen as a coordinating, reducing, and capping agent to effectively direct growth and protect against oxidation of Cu nanowires (Cu NWs) in an aqueous solution. PDA was found to reduce Cu (II) to Cu (I) at room temperature, and stabilize the resulting Cu (I) by forming a coordination complex. The presence of a stable Cu (I) complex is the key step in the synthesis of Cu NWs under mild conditions. Different PDA isomers lead to different growth paths of forming Cu NWs. Both pPDA and mPDA-synthesized Cu NWs were covered with a thin layer of polyphenylenediamine and show excellent anti-oxidation properties, even in the presence of water. The usefulness of the present and electrochemical active Cu NWs for a variety of nanotechnology applications is discussed., Comment: Submitted to Materials and design, Cu nanowires, oxidation

Mitochondrial research is important to ageing, apoptosis, and mitochondrial diseases. In previous works, mitochondria are usually stimulated indirectly by proapoptotic drugs to study mitochondrial development, which is in lack of controllability, or spatial and temporal resolution. These chemicals or even gene techniques regulating mitochondrial dynamics may also activate other inter- or intra-cellular processes simultaneously. Here we demonstrate a photostimulation method on single-mitochondrion level by tightly-focused femtosecond laser that can precisely activate restorable fragmentation of mitochondria which soon recover their original tubular structure after tens of seconds. In this process, series of mitochondrial reactive oxygen species (mROS) flashes are observed and found very critical to mitochondrial fragmentation. Meanwhile, transient openings of mitochondrial permeability transition pores (mPTP), suggested by oscillations of mitochondrial membrane potential, contribute to the scavenging of redundant mROS and recovery of fragmented mitochondria. Those results demonstrate photostimulation as an active, precise and controllable method for the study of mitochondrial oxidative and morphological dynamics or related fields.