The traditional Edisonian process of discovery, which relies on trial and error, is slow and labor intensive. This hinders the development and adoption of technologies that are urgently required for clean energy and environmental sustainability as well for electronic and biomedical device.
Yanliang Zhang is an associate professor of mechanical and aerospace engineering at University of Notre Dame.
“I thought that if we could reduce the time to under a year – or even just a few months – it would be a major game changer for discovering and manufacturing new materials.”
Zhang has created a new 3D printing technology that allows for the production of materials that are unmatchable by traditional manufacturing. The new method mixes aerosolized nanomaterials inks with a single print nozzle and changes the mixing ratio of the inks on the fly. This method, called high-throughput combinational printing (HTCP), controls the 3D architectures of printed materials as well as their local compositions. It also produces materials that have gradient compositions with microscale spatial resolution.
The research has just been published. Nature.
HTCP aerosols are extremely versatile, and can be used with a wide range of materials, including metals, polymers, and biomaterials. It creates combinations of materials that act as “libraries” containing thousands unique compositions.
Zhang stated that the combination of combinational material printing with high-throughput characterization could significantly speed up materials discovery. Zhang’s team has used this method to identify a material with superior thermoelectric characteristics, which is a promising discovery that could be used for energy harvesting or cooling applications.
HTCP also produces materials with a functionally graded transition, from stiff to softer. They are therefore particularly useful for biomedical applications where they need to bridge the gap between soft tissues and rigid wearable or implantable devices.
In the next phase, Zhang and his students at the Advanced Manufacturing and Energy Lab will apply machine-learning and artificial intelligence guided strategies to HTCP’s data-rich nature in order to accelerate discovery and development.
Zhang said, “I hope to develop an automated and self-driving system for materials discovery and manufacturing devices in the future so that the students can focus on high level thinking.”