Electrochemical characterization of single bi-functional nanoparticles and particle ensembles
Kristina Tschulik, Ruhr-Univeristät Bochum, Bochum, Germany
Nanomaterials have been in the focus of research interest for more than a decade. The great potential of electrochemistry to analyse these nanoparticles and their reactivity, in contrast, has not yet been explored in great detail. The ease of sample preparation and the relatively high speed and low cost of electrochemistry make it a useful complementary or even alternative option to the established characterisation methods. Most importantly, the number of particles that can be conveniently analysed ranges from individual particles to ensembles of several tens to more than 108 particles, depending on the electrochemical method used. Here, nanoelectrochemistry is used to characterize bi-functional nanoparticles, a new class of very promising nanomaterials with potential applications ranging from medical treatment to advanced sensing and industrial catalysis. The characterisation of this type of nanomaterials, however, is even more challenging than that of single component particles. Both the particle morphology (size and shape) and subtle compositional differences have to be considered to understand their chemical reactivity.
First, it is demonstrated that cyclic voltammetry of nanoparticle ensembles can be used to determine the composition of bi-functional, two-component nanoparticles. Then it is presented that the shell thickness and quality of core-shell nanoparticles can be determined electrochemically. Using Au–Ag, Fe3O4–Au and Au–SiO2 core-shell nanoparticles as three different examples, the agreement with results obtained by transmission electron microscopy studies is evidenced . In addition to these ensemble studies, single nanoparticle impacts are employed in a third part to measure the size and the shell thickness of individual core-shell nanoparticles using the ‘nano impact’ technique [2,3]. Thus, electrochemistry is established as a tool to both provide statistical average information over large numbers of nanoparticles as well as to measure individual particle-to-particle variations for multicomponent nanoparticle samples.
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