Amplification of particle collision through contact electrification in isotropic turbulence - Danielle R. Johnson; Adam Bocanski; Emily M. Diorio; James Chen; Hui Meng (Proceedings of the National Academy of Sciences, 2025)
Recent discovery of “extreme clustering” of inertial particles in isotropic turbulent flow suggests a hidden mechanism of particle–particle interaction at sub-Kolmogorov separations unexplained by hydrodynamic interaction. The near-contact radial distribution function (RDF) reaches O ( 10 4 ) , resulting in a collision kernel four orders larger than direct numerical simulation predictions. Statistical stationarity is lost in the particle-laden turbulence, suggesting the particles experience a nonequilibrium process. We hypothesize dielectric particles in isotropic turbulence experience contact electrification through interparticle collisions, creating inhomogeneous mosaic surface charge. These mosaic charges lead to attractive forces and thereby extreme clustering and collision amplification, forming a positive feedback loop. To explore this potential mechanism, we investigated hollow glass spheres dispersed in a high-Reynolds-number homogeneous isotropic air turbulence chamber using high-resolution 3D particle tracking velocimetry and Kelvin Probe Force Microscopy (KPFM). We measured RDF, particle-pair mean-inward radial relative velocity, and mean radial relative acceleration (RA) with time up to 10 min. We sampled particles from the flow chamber through time and evaluated their nanoscopic charge distribution using KPFM. We found that both RDF and mosaic surface charge increase with time; RA at close separations is attractive, intensifies as particles approach, and grows in time; and the turbulence-exposed RA curves collapse when nondimensionalized by the dipole–dipole acceleration calculated from mosaic charge distributions. These results support the proposed mechanism—Inhomogeneous Mosaic Potential Amplified Collisions in Turbulence (IMPACT). Better understanding and modeling of these effects could improve predictions for air pollution, weather patterns, and drug manufacturing—where particle interactions have big impacts.