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Comprehensive Summary: Charge transport characterization of single‐molecule junctions is essential for the fundamental research of single‐molecule physical chemistry and the development towards single‐molecule electronic devices and circuits. Among the single‐molecule conductance characterization techniques, the single‐molecule break junction technique is widely used in tens of worldwide research laboratories which can generate a large amount of experimental data from thousands of individual measurement cycles. However, data interpretation is a challenging task for researchers with different research backgrounds, and the different data analysis approaches sometimes lead to the misunderstanding of the measurement data and even reproducibility issues of the measurement. It is thus a necessity to develop a user‐friendly all‐in‐one data analysis tool that automatizes the basic data analysis in a standard and widely accepted way. In this work, we present the XMe Code (Xiamen Molecular Electronics Code), an intelligent all‐in‐one data analysis tool for the comprehensive analysis of single‐molecule break junction data. XMe code provides end‐to‐end data analysis that takes in the original experimental data and returns electronic characteristics and even charge transport mechanisms. We believe that XMe Code will promote the transparency of the data analysis in single‐molecule electronics and the collaborations among scientists with different research backgrounds. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Charge transport characterization of single‐molecule junctions is essential for the fundamental research of single‐molecule physical chemistry and the development towards single‐molecule electronic devices and circuits. Among the single‐molecule conductance characterization techniques, the single‐molecule break junction technique is widely used in tens of worldwide research laboratories which can generate a large amount of experimental data from thousands of individual measurement cycles. However, data interpretation is a challenging task for researchers with different research backgrounds, and the different data analysis approaches sometimes lead to the misunderstanding of the measurement data and even reproducibility issues of the measurement. It is thus a necessity to develop a user‐friendly all‐in‐one data analysis tool that automatizes the basic data analysis in a standard and widely accepted way. In this work, we present the XMe Code (Xiamen Molecular Electronics Code), an intelligent all‐in‐one data analysis tool for the comprehensive analysis of single‐molecule break junction data. XMe code provides end‐to‐end data analysis that takes in the original experimental data and returns electronic characteristics and even charge transport mechanisms. We believe that XMe Code will promote the transparency of the data analysis in single‐molecule electronics and the collaborations among scientists with different research backgrounds. [ABSTRACT FROM AUTHOR]

In this paper, the set-membership state and parameter estimation problems are considered for linear time-varying systems. The noises in the system are unknown but bounded and the variation of parameters is considered as a bounded expansion. The entire estimator is an interactive estimation of parameters and states. In parameter estimation, the exact set of parameter estimates may be non-convex because of the uncertain states. A polytopic linear parameter varying (LPV) enclosure of the system regression expression is constructed as a convex relaxation. The result of parameter estimation is a set to include the true parameter of the model. Meanwhile, in the state estimation, a similar LPV enclosure of the state-space model is also used to bound the state matrix uncertainties. This LPV enclosure in the state estimation is narrowed by the parameter estimation, and then the set of state estimates is computed to include the true state. The constrained zonotope (CZ) is used to describe sets of interest and the necessary set operations are expanded to LPV mapping. Because of the highly adjustable accuracy of CZ, the proposed algorithm can make a better trade-off between computational accuracy and efficiency. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: In this study, the Koopman operator is applied to solve the challenging nonlinear set‐membership (SM) state estimation problem. The basic idea is to lift the nonlinear system into a linear one with a higher dimension, and then linear SM estimation methods can be adopted. The Koopman operator is an infinite‐dimensional linear decomposition of nonlinear dynamics. This linearized system can fully describe the state evolution of the nonlinear dynamics with input, output, and noises. To apply SM state estimation algorithms, a finite‐dimensional approximation is obtained by using the data‐driven method. Meanwhile, the statistical properties of the approximation errors are also utilized to reduce the conservativeness of the filter. The probability distributions of approximation errors are described by sample particles. Coupled with the unknown but bounded noises, the linearized system is associated with two types of uncertainties. To this end, the merging SM and stochastic strategy is adopted to construct a class of confidence state set whose level approaches 1. After that, the optimal filter gain is given to minimize the size of the confidence state set. Finally, three numerical examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Artificial sensory nerves can simulate the functions of visual perception and information processing of the human brain, which is indispensable for the immediate visualization of the environment and awareness requisite to avoid the potential harm. However, it still remains a long‐standing challenge to integrate recognition learning with real‐time processing function in a single device. Herein, an interface defects‐tuning strategy is proposed for developing a novel bi‐functional phototransistor. Benefiting from the tuned polymer‐perovskite layer‐heterointerface defects, the photogenerated charge trapping and de‐trapping capacities are significantly improved, thereby facilitating the photogenerated carrier separation and injection. The prepared organic‐inorganic hybrid phototransistors can perfectly mimic the human visual synaptic behaviors with a quick response speed (<35 ms, far below that of human eyes to incident light (225 ms)). More importantly, the phototransistor exhibited obvious visual adaptation toward wide‐ranging light stimuli, and realized unique desensitization to simulate the self‐protection behavior of the human visual systems. Specifically, the adaptation timescales of the device under dim and high light conditions are superior to bio‐systems (<2 min). Consequently, the reported strategy for the preparation of bi‐functional phototransistors provides a brand‐new perspective for next‐generation artificial nervous systems. [ABSTRACT FROM AUTHOR]