A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles

Abstract

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 can reliably optimise the conditions to produce gold-nanoparticles over many cycles, discovering new synthetic conditions for known nanoparticle shapes 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 independent cycles of evolution we show our autonomous system can produce spherical nanoparticles, rods, and finally octahedral nanoparticles by using our optimized rods as seeds.

High Antioxidant Activity and Low Toxicity of Gold Nanoparticles Synthesized With Virola Oleífera: An Approach Using Design of Experiments

“…The AuNPs -pH 4.7 showed an aspect ratio of 0.905892, presenting a higher sphericity, and the AuNPs -pH 7.7 and AuNPs -pH 10.7, presented values of 0.844786 and 0.710228 respectively, showing a lower sphericity [18,19] (Fig. 3F).…”

Automated robotic platforms in design and development of formulations

“…55,61 Being developed for different purposes, these platforms are only able to produce one sample at a time with interstage automated cleaning of the reactionware/containers. An attempt to overcome this limitation can be found in the recent studies, 62 where an automated rotating wheel, coupled with a 3D-printed dispensing element and automated syringe pumps, can allocate batches of 24 vials per run. The potential of using 3D printing technologies to build inexpensive hardware was also highlighted.…”

Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow

“…Generally, experimentation strategies with high reliance on user guidance and operation, require increased time commitment and level of expertise. Recent advances in AI, including deep neural networks (DNN) and reinforcement learning, for rapid chemical space exploration, [49][50][51][52][53][54][55][56][57][58][59][60][61] provide an exciting opportunity to reshape the development and on-demand manufacturing of colloidal nanomaterials. Consequently, a number of self-optimizing microfluidic reactors have been developed to take advantage of continuous material exploration in a low chemical consumption system.…”