Tripartite Transformation of Silver Nanoparticles: Deciphering Dynamic Toxicity through Mass Cytometry and Machine Learning

Environmental risk assessment of silver nanoparticles (AgNPs) has largely relied on a static binary framework attributing toxicity to either AgNPs or released ions (Ag+), overlooking secondary AgNPs (Ag0NPs) regenerated via environmental reduction. Here, we develop an integrated framework combining AgNP tracers, mass cytometry, and machine learning to resolve the time-dependent interconversion among AgNPs, Ag+, and Ag0NPs and to deconvolute their toxic contributions in microalgae. A pronounced size-dependent kinetic divergence was observed: 50 nm AgNPs released Ag+ 1.5 times faster than 90 nm particles, causing stronger early ionic toxicity, but subsequently produced a higher fraction of Ag0NPs (36.8% vs 25.3% at 72 h). Toxicity shifted from Ag+-dominated effects at early exposure (51.5–82% at 36 h) to substantial contributions from particulate species under prolonged exposure (up to ∼40%). These results demonstrate that AgNP toxicity emerges from a dynamic tripartite transformation system, providing a mechanistic basis for improved environmental risk assessment of metastable nanomaterials.