Nanoplasmonic Sensing for Materials Science

Doktorsavhandling, 2013

With the rising importance of nanoscience and nanotechnology, there is a need for new sensitive and easy-to-use characterization techniques able to follow processes at the nanoscale. In this thesis different aspects of nanoplasmonic sensing for studying materials science processes at the nanoscale are demonstrated and discussed for the following model systems: oxidation/corrosion of Al and Cu and the solid-liquid phase transition of Sn. Nanoplasmonic sensing relies on the excitation of localized surface plasmons (LSPR) in metal nanoparticles. The resonance details are very sensitive to optical property changes in/on the nanoparticles themselves or in their nano- scale neighborhood, e. g., surface oxidation/corrosion. The corrosion of Al and Cu nanoparticles and thin films was studied using nanoplasmonic sensing in various environments like dry and humid air and liquid water and (for Cu) with and without a corrosion inhibitor. Corrosion kinetics were measured with submonolayer sensitivity – even in the case of very slow corrosion, such as in mildly oxidizing environments and when the metal surface was protected by a corrosion inhibitor. The solid-liquid (melting-freezing) phase transition in Sn nanoparticles was investigated by nanoplasmonic sensing. The undercooling as well as the melting and freezing kinetics were measured and analyzed theoretically. In order to gain broad information about studied systems, it is often desirable to combine several techniques in situ, with the same sample. Nanoplasmonic sensing is very suitable for such combinations. Here, experimental integration was realized of nanoplasmonic sensing with quartz crystal microbal- ance with dissipation monitoring (QCM-D) and with vibrational sum frequency spectroscopy (VSFS). This thesis demonstrates that nanoplasmonic sensing is a highly sensitive, fast, easy-to-use, and versatile technique that can be used to monitor a variety of processes in materials science in situ and in real time.