Multi-dimensional analysis of single particles with sequential dual-nanopipette sensors

Abstract

Nanoparticles play crucial roles in a wide range of scientific and industrial fields. Analysing the physicochemical characteristics of nanoparticles is of paramount importance in identifying and classifying different particles. However, due to their small size and inherent heterogeneity, comprehensive analysis of the size and shape, especially the complex surface characters, of individual particles is still confronted with challenges. In this work, we reported a facile strategy to construct a sequential dual-nanopipette sensor (SDNS) with two sensing interfaces for multi-parameter detection of nanoparticles at the single particle level. The collaboration of the two sensing interfaces enabled dual recognition of the same particle, which improved the detection accuracy effectively. With this platform, it was not only possible to perform concentration quantification and size characterization, but also effectively classify the particles via dual determination of the surface charge or the biochemical components of the particles. As the sensing interfaces of SDNS can be varied to meet the detection requirements, this approach can be easily adjusted to analyse more kinds and dimensions of surface characters of single nanoparticles, which opens up new avenues for the exploration of biological or other kinds of nanoparticles as well as the deeper comprehension of particle functions and evolution.