Atomic-scale ultrafast laser processing of nanocrystal with plasmonic modulation for machine learning combined flexible sensor devices

Abstract Nanometallic materials have attracted wide research attention in the fabrication of functional devices, including flexible electronics circuits and high-sensitive sensors. Sintering of nanometallic materials is generally thought as an effective technology for the functional manufacturing, and the controllable sintering of nanometallic materials and its major mechanisms have long been a challenge. Here, an ultrafast laser processing strategy for Ag nanoparticles (NPs) is achieved by modulating plasmonic. The excitation mode of plasmon can be designed by laser parameters, including polarization with a specific crystal size. The atomic-scale ultrafast dynamics are revealed for understanding the sintering process and design of the sintered structures. The non-equilibrium energy transfer between electron and lattice and dynamic evolution of pressure are proved to be the foremost driving forces on the motion of atomic structures. Through research of plasmonic-induced electric field enhancement and non-uniform deposition of heat and in-situ observation of relative transmittance, mapping from atomic-scale structure to micro behavior is established. Based on plasmonic modulation and processing of Ag NPs, a machine learning combined flexible gesture sensor with high recognition accuracy is displayed. This work expands the knowledge of interactions between lasers and nanometallic materials and provides a method for designing functional devices for a wide range of applications.