Nanomachines and DNA Nanotech (DNA4reNOMS)

Micro- and nano-electromechanical systems (MEMS, NEMS) have enabled highly successful technologies, encompassing acceleration sensors, inkjet cartridges, digital mirror projectors, and more. However, despite decades of chip development in the semiconductor industry, fabrication and energy costs remain high with poor materials utilisation and we lack devices that are reconfigurable on the nanometer scale. In contrast to these top-down approaches, DNA nanotechnology has emerged as a powerful bottom-up approach to assemble sophisticated structures in solution with molecular precision. This project adopts DNA architectures to develop functional nano-opto-mechanical devices (NOMS), which can be reconfigured in-situ. Rather than electronics which is less compatible with DNA origami, we harness optics as it allows both global and local addressing, as well as ultrasensitive single nanostructure readout. The many challenges involved demand a concerted programme with different expertise combining DNA assembly, colloidal architecture, nanostructuring, and photonics. The project is based on new ideas to produce optical forces, new concepts in mechanically-bistable DNA constructs, and new hierarchical interactions between nanocomponents, with the goal to build sensors embedded in living tissue that cover wide force regimes, metasurfaces producing opto-switchable near- and far-fields, mechanical amplified single-molecule sensor arrays, and artificial muscles. Current tech devices are atom inefficient, discarded after use, and cannot be efficiently recycled. A radically new approach is required in the long term, which is atom efficient. Our objective is a new generation of general nanomachines and devices that are reconfigurable and reconstructable, capable of disassembly before rebuilding alternatives. It will generate a viable route to nanomachinery stimulating a host of scientific and technological endeavours, while being compatible with a sustainable future manufacturing.

This project is funded by an ERC Synergy grant and is in collaboration with the groups of Jeremy Baumberg at Cambridge and Tim Liedl at the TUM.