(Nanowerk News) The trial-and-error process of Edison’s time-honored inventions was slow and labor-intensive. This hinders the development of new technologies that are urgently needed for clean energy and environmental sustainability, as well as for electronic and biomedical devices.
“Usually it takes 10 to 20 years to find a new material,” said Yanliang Zhang, professor of aerospace and mechanical engineering at the University of Notre Dame.
“I think 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 manufacture of new materials.”
Now Zhang has done it, creating a new 3D printing method that produces materials in a way that conventional manufacturing can’t match. This new process mixes multiple aerosol nanomaterial inks in a single printing nozzle, varying the mixing ratio of the ink rapidly during the printing process. This method — called high-throughput combinatorial printing (HTCP) — controls both the 3D architecture of the printed material and local composition and produces materials with gradient composition and properties at micro-scale spatial resolution.
His research was just published in Natural (“High yield printing of combinatorial materials from aerosols”).
Aerosol-based HTCP is extremely versatile and applicable to a wide range of metals, semiconductors and dielectrics, as well as polymers and biomaterials. This results in combinational materials that serve as “libraries”, each containing thousands of unique compositions.
Combining combinational materials printing and high-throughput characterization can significantly accelerate materials discovery, said Zhang. His team has used this approach to identify semiconductor materials with superior thermoelectric properties, discoveries that hold promise for energy harvesting and cooling applications.
In addition to accelerating discovery, HTCP produces functionally graded materials that gradually transition from stiff to soft. This makes them especially useful in biomedical applications that need to bridge between soft body tissues and wearable and implantable hardware.
In the next research phase, Zhang and students at the Advanced Manufacturing and Energy Lab plan to apply machine learning and artificial intelligence-guided strategies to the data-rich properties of HTCP to accelerate the discovery and development of various materials.
“In the future, I hope to develop autonomous and independent processes for material discovery and device manufacturing, so that students in the lab can be free to focus on higher-order thinking,” said Zhang.