Abstract
The biofunctionalization of particles with specific targeting moieties forms the foundation for molecular recognition in biomedical applications such as targeted nanomedicine and particle-based biosensing. To achieve a high precision of targeting for nanomedicine and high precision of sensing for biosensing, it is important to understand the consequences of heterogeneities of particle properties. Here, we present a comprehensive methodology to study with experiments and simulations the collective consequences of particle heterogeneities on multiple length scales, called superpositional heterogeneities, in generating reactivity variability per particle. Single-molecule techniques are used to quantify stochastic, interparticle, and intraparticle variabilities, in order to show how these variabilities collectively contribute to reactivity variability per particle, and how the influence of each contributor changes as a function of the system parameters such as particle interaction area, the particle size, the targeting moiety density, and the number of particles. The results give insights into the consequences of superpositional heterogeneities for the reactivity variability in biomedical applications and give guidelines on how the precision can be optimized in the presence of multiple independent sources of variability.