School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK.
Nat Commun. 2020 Jun 2;11(1):2771. doi: 10.1038/s41467-020-16501-4.
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.
自上而下的纳米材料制造方法可以提供精确的结构，但成本高昂，而自下而上的组装方法则是通过反复试验发现的。大自然通过自主利用 DNA 突变来改进和传递蓝图来实现材料发现。遗传启发式优化已在一系列应用中得到应用，从催化到发光材料，但这些应用不是自主的，也不使用物理突变。在这里，我们提出了一种自主驱动的材料进化机器人平台，它可以可靠地优化条件，以在多个周期内生产金纳米粒子，利用光电特性作为驱动，为已知纳米粒子形状发现新的合成条件。我们不仅可以可靠地发现一种方法，该方法以数字方式编码来合成这些材料，我们还可以在前几代材料中播种，以设计更复杂的结构。在三个独立的进化周期中，我们展示了我们的自主系统可以通过使用优化的棒作为种子来生产球形纳米粒子、棒状纳米粒子，最后是八面体纳米粒子。
