IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-16501-4.html
   My bibliography  Save this article

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

Author

Listed:
  • Daniel Salley

    (University of Glasgow)

  • Graham Keenan

    (University of Glasgow)

  • Jonathan Grizou

    (University of Glasgow)

  • Abhishek Sharma

    (University of Glasgow)

  • Sergio Martín

    (University of Glasgow)

  • Leroy Cronin

    (University of Glasgow)

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.

Suggested Citation

  • Daniel Salley & Graham Keenan & Jonathan Grizou & Abhishek Sharma & Sergio Martín & Leroy Cronin, 2020. "A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16501-4
    DOI: 10.1038/s41467-020-16501-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-16501-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-16501-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amanda A. Volk & Robert W. Epps & Daniel T. Yonemoto & Benjamin S. Masters & Felix N. Castellano & Kristofer G. Reyes & Milad Abolhasani, 2023. "AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Amanda A. Volk & Milad Abolhasani, 2024. "Performance metrics to unleash the power of self-driving labs in chemistry and materials science," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    3. Benjamin P. MacLeod & Fraser G. L. Parlane & Connor C. Rupnow & Kevan E. Dettelbach & Michael S. Elliott & Thomas D. Morrissey & Ted H. Haley & Oleksii Proskurin & Michael B. Rooney & Nina Taherimakhs, 2022. "A self-driving laboratory advances the Pareto front for material properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16501-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.