Discovering of Atomically Precise Metal Nanoclusters by High-Throughput Syntheses Platform

Area of Science:

• Materials Science
• Nanotechnology
• Inorganic Chemistry

Background:

• Understanding noble metal nanocluster structures is crucial for scientific and practical applications.
• Low synthetic efficiency has historically limited nanocluster research and development.

Purpose of the Study:

• To develop high-throughput synthesis methods for noble metal nanoclusters.
• To accelerate the discovery and optimization of nanocluster synthesis routes.

Main Methods:

• High-throughput screening of nanocluster reactions in dichloromethane at room temperature.
• Machine learning for analyzing experimental data and guiding synthesis optimization.
• Synthesis of homo-gold, homo-copper, and gold-copper alloy nanoclusters.

Main Results:

• Successful high-throughput synthesis of three nanoclusters in a single reaction: Au18(SC6H11)14, [Au41Cu66(SC6H11)44](SbF6)3, and a copper nanocluster (Cu-NC).
• Optimized synthesis routes using machine learning.
• Guided synthesis of additional nanoclusters including Au40Cu34, [Au6Cu6]n, and Au18Cu32.

Conclusions:

• High-throughput screening is an effective strategy for accelerating nanocluster synthesis.
• Machine learning integration enhances experimental data analysis and synthesis optimization.
• This approach significantly advances the development of diverse noble metal nanoclusters.