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, environmental sustainability and electronic and biomedical device devices.
“It usually takes 10 to 20 years to discover a new material,” said Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame.
“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.”
Now Zhang has done just that, creating a novel 3D printing method that produces materials in ways that conventional manufacturing can’t match. 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 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.
His research was just published in Nature.
The HTCP aerosol is highly versatile, and can be used with a wide range of materials, including metals, dielectrics, polymers, and biomaterials. It generates combinational materials that function as “libraries,” each containing thousands of 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’s functionally graded materials, which gradually change from stiffness to softness, not only speed up the discovery process but also allow for faster material development. These materials are particularly useful in biomedical devices that bridge soft tissues with stiff wearables and implants.
In the next phase of research, 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 in order 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,” Zhang said.