By using self-assembly as the basis for our fabrication we ensure our developed nanotechnologies are scalable whilst still offering the highest field enhancements.
Using advanced machine learning methods large datasets can be rapidly processes, allowing us to study the behaviour of metals and molecules at heterogeneous interfaces.
Optical properties of self-assembled nanoarchitectures can be tuned readily tuned. This allows for great versatility and optimisation in optically driven processes such as photocatalysis and in sensing applications.
Understanding molecular interactions on the nanoscale is vital for rational design of new nanotechnologies. Especially when binding of reagents and analytes is involved.
To accurately map out the behaviour of nanomaterials it is essential that first highly reliable nanoarchitectures are made. This can be achieved through self-assembly and by using rigid molecular spacers.
