Abstract Scope |
Metal nanoclusters can have remarkable properties, including fluorescence, self-assembled 2D crystalline morphologies, anti-microbial activity, and useful magnetic properties. Nucleating and stabilizing these metal nanoclusters with desired properties has been challenging, but polymers, particularly short strands of DNA (6-30 nucleotides long) that serve no biological function, appear promising. These DNA sequences are highly tailorable, but establishing the connection between the sequence used, the resulting nanoclusters, and resulting properties remains particularly challenging, due in part to the complexity of short DNA sequences. This talk will present an approach using machine learning and combinatorial design to identify polymer structures and compositions which can be correlated to families of nanoclusters likely to yield valuable materials properties. |