Self-Driving Laboratory for Accelerated On-Surface Synthesis under Ultrahigh Vacuum

Area of Science:

• Materials Science
• Robotics
• Artificial Intelligence

Background:

• Automated experimentation accelerates materials synthesis.
• Many critical synthesis processes require ultrahigh vacuum (UHV) environments with strict limitations.
• Existing automated platforms typically operate under atmospheric conditions, posing challenges for UHV-based synthesis.

Purpose of the Study:

• To present a novel self-driving synthetic platform designed for UHV environments.
• To address the challenges associated with UHV-based materials synthesis.
• To enable dynamic adjustment of experimental parameters for optimizing material performance and reducing experimental cycles.

Main Methods:

• Integration of robotics, automation, and machine learning.
• Development of a self-driving platform capable of operating in UHV conditions.
• Dynamic adjustment of experimental parameters such as annealing temperature, time, and molecular coverage.

Main Results:

• Successful on-surface synthesis of graphene nanoribbons (GNRs).
• Achieved an average GNR length of approximately 20 nm in only 12 experimental cycles.
• Demonstrated optimization of annealing temperature, time, and molecular coverage for efficient synthesis.
• Characterization of synthesized GNRs using high-resolution scanning tunneling microscopy (STM).

Conclusions:

• The developed self-driving platform effectively overcomes UHV synthesis challenges.
• The platform significantly reduces the number of experiments required to achieve optimal material properties.
• This technology has broad applicability beyond carbon nanostructures to other UHV-dependent synthesis systems.