Automated Benchtop Synthesis of a Quadrillion-Plus Member Core@Multishell Nanoparticle Library Using a Massively...

Area of Science:

• Materials Science
• Nanotechnology
• Chemical Engineering

Background:

• Computational materials science predicts vast numbers of novel materials, but experimental synthesis lags behind.
• Current automated nanoparticle synthesis struggles with complex compositions and generalizability.
• A significant gap exists between theoretical material discovery and practical synthesis.

Purpose of the Study:

• To develop a massively generalizable automated synthesis platform for complex core@multishell nanoparticles.
• To bridge the gap between computational material prediction and experimental realization.
• To demonstrate the platform's capability using rare earth oxychloride (REOCl) nanoparticles.

Main Methods:

• Integrated a computer-controlled pump system with a laboratory-scale synthesis setup.
• Developed a generalizable reaction for growing up to 20 REOCl shells in any sequence.
• Programmed reagent injection for composition gradients, dopants, and solid solutions.
• Utilized ChatGPT for automated target selection and synthesis direction.

Main Results:

• Successfully synthesized over 651 quadrillion distinct core@multishell nanoparticles from seven isostructural REOCl compounds.
• Demonstrated control over shell sequence, composition, and doping.
• Achieved near-infinite scope in nanoparticle library generation.
• Showcased automated synthesis of complex nanoparticle targets selected by AI.

Conclusions:

• The developed automated platform enables massively generalizable synthesis of complex nanoparticles.
• This approach significantly accelerates the translation of computational predictions into synthesized materials.
• The platform is poised for integration into autonomous materials discovery workflows.