Molecular junctions are building blocks for constructing future nanoelectronic devices that enable the investigation of a broad range of electronic transport properties within nanoscale regions. Crossing both the nanoscopic and mesoscopic length scales, plasmonics lies at the intersection of the macroscopic photonics and nanoelectronics, owing to their capability of confining light to dimensions far below the diffraction limit. Research activities on plasmonic phenomena in molecular electronics started around 2010, and feedback between plasmons and molecular junctions has increased over the past years. These efforts can provide new insights into the near-field interaction and the corresponding tunability in properties, as well as resultant plasmon-based molecular devices. This Review presents the latest advancements of plasmonic resonances in molecular junctions and details the progress in plasmon excitation and plasmon coupling. We also highlight emerging experimental approaches to unravel the mechanisms behind the various types of light-matter interactions at molecular length scales, where quantum effects come into play. Finally, we discuss the potential of these plasmonic-electronic hybrid systems across various future applications, including sensing, photocatalysis, molecular trapping and active control of molecular switches.

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This paper presents a novel and general distributed acoustic sensing (DAS) signal recognition framework aimed at real-time detection and classification of intrusion in the space-time domain. The framework is based on the combination of a convolution neural network (CNN) and a long short-term memory network (LSTM). The convolutional structure extracts the spatial features from multi-channel signals of the DAS system, while the LSTM network analyzes the temporal relationships over time. The framework can be deployed on high-speed railways for real-time intrusion threat detection, which is one of the most urgent and challenging problems that needs to be resolved as there is an increasing demand for high detection and low false alarm rates, and short response time. The alarm sensitivity and specificity of the framework are controlled by user-set parameters. A real field experiment is conducted in a strong background noise scenario and an intrusion threat detection rate of 85.6%, with only 8.0% false alarm rate is achieved. For threat classification, the average threat detection rate is 69.3%, and the average false alarm rate is 13.2%. Owing to the high detection accuracy of the framework, the average detection response time is shortened to 8.25 s.

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