The sensitivity of energetic materials is strongly influenced by environmental humidity, yet the underlying atomic-scale mechanisms remain unclear due to contradictory experimental and computational findings. Herein, we present a multiscale investigation combining machine learning-accelerated molecular dynamics (MD) with near-ab initio accuracy and experimental measurement, focusing on the hygroscopic explosive NTO. Large-scale MD simulations of NTO/H2O systems under thermal, impact, and friction stimuli identify two key desensitization mechanisms: an energy-resistance effect, where H2O molecules absorb energy and suppress hot spot formation, delaying NTO decomposition, and an interfacial lubrication effect, where water reduces interfacial binding energy and friction, decreasing mechanical energy absorption and increasing the reaction initiation threshold. Experiments via laser-induced plasma spectroscopy (LIPS) further confirm that NTO samples conditioned at higher humidity exhibit reduced impact and friction sensitivities. This work unravels a coupled thermal–mechanical–chemical pathway of moisture-induced desensitization, offering critical insights for understanding its inherent mechanism.
Wang et al. (Tue,) studied this question.