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A Nanomaterials Discovery Robot for the Darwinian Evolution of Shape Programmable Gold Nanoparticles

submitted on 12.06.2019, 20:28 and posted on 13.06.2019, 14:58 by Graham Keenan, Daniel Salley, Sergio Martin, Jonathan Grizou, Abhishek Sharma, Leroy Cronin

The fabrication of nanomaterials from the top-down gives precise structures but it is costly, whereas bottom-up assembly methods are found by trial and error. Nature evolves materials discovery by refining and transmitting the blueprints using DNA mutations autonomously. Genetically inspired optimisation has been used in a range of applications, from catalysis to light emitting materials, but these are not autonomous, and do not use physical mutations. Here we present an autonomously driven materials-evolution robotic platform that allows us to reliably discover the conditions to produce gold-nanoparticles that can run for many cycles, discovering entirely new systems using the opto-electronic properties as a driver. Not only can we reliably discover a method, encoded digitally to synthesise these materials, we can seed in materials from preceding generations to engineer more sophisticated architectures. Over three cycles of evolution we show the seeds from each generation can produce spherical nanoparticles, rods, and highly anisotropic arrow-faceted nanoparticles.


Email Address of Submitting Author


University of Glasgow



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no conflict of interest