Yanliang Zhang creates new materials using 3D printing
The novel method, developed by the associate professor of aerospace and mechanical engineering at the University of Notre Dame, Indiana, USA, mixes multiple aerosolized nanomaterial inks in a single printing nozzle
Yanliang Zhang, an associate professor of aerospace and mechanical engineering at the University of Notre Dame, Indiana, USA, has reportedly developed a novel 3D printing method that produces materials in ways not possible using conventional manufacturing methods, according to an article published by Karla Cruise.
“It usually takes 10 to 20 years to discover a new material,” said Yanliang Zhang. “I thought if we could shorten that time to less than a year – or even a few months – it would be a game changer for the discovery and manufacturing of new materials.”
His new process mixes multiple aerosolized nanomaterial inks in a single printing nozzle, varying the ink mixing ratio during the printing process. This method – called high-throughput combinatorial printing (HTCP) – controls both the printed materials’ 3D architectures and local compositions, and produces materials with gradient compositions and properties at microscale spatial resolution. The research has been published in Nature.
The aerosol-based HTCP is extremely versatile and applicable to a broad range of metals, semiconductors, and dielectrics, as well as polymers and biomaterials. It generates combinational materials that function as ‘libraries’ – each containing thousands of unique compositions.
According to Yanliang Zhang, combining combinational materials printing and high-throughput characterization can significantly accelerate materials discovery. His team at the University of Notre Dame has already used this approach to identify a semiconductor material with superior thermoelectric properties – a promising discovery for energy harvesting and cooling applications.
In addition to speeding up discovery, HTCP produces functionally-graded materials that gradually transition from stiff to soft. This makes them particularly useful in biomedical applications that need to bridge between soft body tissues and stiff wearable and implantable devices.
In the next phase of research, Yanliang Zhang and the students in his Advanced Manufacturing and Energy Lab plan to apply machine learning and artificial intelligence-guided strategies to the data-rich nature of HTCP to accelerate the discovery and development of a broad range of materials.
“In the future, I hope to develop an autonomous and self-driving process for materials discovery and device manufacturing, so students in the lab can be free to focus on high-level thinking,” said Yanliang Zhang.
