Single-Particle Recognition and Semiquantification of Coal-Derived Particles by ToF-SIMS Imaging

To address the technical need for the precise detection and semiquantitative analysis of coal-derived particulate matter in complex soil matrices, this study optimized and established a comprehensive detection method based on time-of-flight secondary ion mass spectrometry (ToF-SIMS) and developed a semiquantitative analysis strategy utilizing AI-assisted single-particle size-resolved image recognition. Through systematic optimization of experimental conditions, it was determined that C4H3– is the characteristic ion of coal-derived particles, while Na+ and Ca+ are the characteristic ions of noncoal-derived (soil mineral) particles. Ethanol-dispersed monocrystalline silicon wafers were selected as the optimal substrate, and Bi3+ as the ion source. Since bright spots characteristic of coal-derived particles could not be characterized on secondary ion images under positive ion conditions but could be detected in negative ion mode, the negative ion mode was adopted as the testing condition for time-of-flight secondary ion mass spectrometry (ToF-SIMS). Through multiple rounds of human-machine interaction, parameters for the AI-assisted counting method were established. This method was applied to the analysis of soil samples from 10 typical sampling sites in the Tongting mining area, and the results for coal-derived particulate matter were validated against the gold standard GC-MS. The detection method and semiquantitative strategy established in this study provide a feasible technical pathway for the efficient and accurate detection and characterization of coal-derived particulate matter in complex matrices, holding significant practical value.