Trace detection of explosives has been an ongoing challenge for decades and has become one of several critical problems in defense science; public safety; and global counter-terrorism. As a result, there is a growing interest in employing a wide variety of approaches to detect trace explosive residues. Spectroscopy-based techniques play an irreplaceable role for the detection of energetic substances due to the advantages of rapid, automatic, and non-contact. The present work provides a comprehensive review of the advances made over the past few years in the fields of the applications of terahertz (THz) spectroscopy; laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy; and ion mobility spectrometry (IMS) for trace explosives detection. Furthermore, the advantages and limitations of various spectroscopy-based detection techniques are summarized. Finally, the future development for the detection of explosives is discussed.

Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.