Fully automatic transfer and measurement system for structural superlubric materials

Abstract Structural superlubricity (SSL), a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in the fields of MEMS devices, mechanical engineering, and energy resources. A critical step towards the practical application of SSL is the massive transfer and high throughput performance evaluation. As SSL systems are heterogeneous, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and mechanical performance measurement for SSL materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with SSL properties. It can also complete the self-retraction force measurement of an individual graphite flake within 30 s, which is five times faster than manual operation. For fabrication, the system can complete the 10×10 graphite flakes assembly array and form various pre-designed patterns within 100 mins without manual intervention, which is 15 times faster than manual operation. The high accuracy, efficiency, robustness, and scalability of this automated system open up new possibilities in micron-scale experiments and manufacturing, and they pave the way for the construction of a database for the genetic engineering of micron materials.