Triboelectrification is a well-known phenomenon that commonly occurs in nature and in our lives at any time and any place. Although each and every material exhibits triboelectrification, its quantification has not been standardized. A triboelectric series has been qualitatively ranked with regards to triboelectric polarization. Here, we introduce a universal standard method to quantify the triboelectric series for a wide range of polymers, establishing quantitative triboelectrification as a fundamental materials property. By measuring the tested materials with a liquid metal in an environment under well-defined conditions, the proposed method standardizes the experimental set up for uniformly quantifying the surface triboelectrification of general materials. The normalized triboelectric charge density is derived to reveal the intrinsic character of polymers for gaining or losing electrons. This quantitative triboelectric series may serve as a textbook standard for implementing the application of triboelectrification for energy harvesting and self-powered sensing.

Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.

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Electronic defects and grain boundaries of perovskite films will significantly deteriorate both the efficiency and the stability of perovskite solar cells (PSCs), and various methods aimed to reduce these defects are proposed. Herein, an organic solid molecule of pyridinedicarboxylic acid (PDA) with one pyridine and two carboxylic acid groups is used as a passivating agent to cure the defects by regulating the perovskite microstructures in a multiple manner. The defects located at both the surfaces and grain boundaries of polycrystalline MAPbI3 perovskites are simultaneously passivated through the multiple coordination effects between the used functional groups and uncoordinated Pb2+, regardless of the substitution sites of the carboxylic acid and pyridine. Impressively, the PDA‐passivated inverted PSCs achieve remarkably enhanced power conversion efficiencies (PCEs) from 16.43% to nearly 19% and maintain over 90% of its original PCE after 1300 h under an inert environment. These findings indicate that the commercially available PDA molecule emerges as an efficient passivating agent of perovskite defects capable of stimulating the combined effects of the multiple functional groups, which is highly promising for the practical applications of PSCs with both high efficiency and good stability.

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Abstract-Consider a full-duplex (FD) bidirectional secure communication system, where two communication nodes, named Alice and Bob, simultaneously transmit and receive confidential information from each other, and an eavesdropper, named Eve, overhears the transmissions. Our goal is to maximize the sum secrecy rate (SSR) of the bidirectional transmissions by optimizing the transmit covariance matrices at Alice and Bob. To tackle this SSR maximization (SSRM) problem, we develop an alternating difference-of-concave (ADC) programming approach to alternately optimize the transmit covariance matrices at Alice and Bob. We show that the ADC iteration has a semi-closed-form beamforming solution, and is guaranteed to converge to a stationary solution of the SSRM problem. Besides the SSRM design, this paper also deals with a robust SSRM transmit design under a moment-based random channel state information (CSI) model, where only some roughly estimated first and second-order statistics of Eve's CSI are available, but the exact distribution or other high-order statistics is not known. This moment-based error model is new and different from the widely used bounded-sphere error model and the Gaussian random error model. Under the consider CSI error model, the robust SSRM is formulated as an outage probability-constrained SSRM problem. By leveraging the Lagrangian duality theory and DC programming, a tractable safe solution to the robust SSRM problem is derived. The effectiveness and the robustness of the proposed designs are demonstrated through simulations.

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