An artistic representation of high-throughput combinatorial printing. Image: University of Notre Dame.The time-honored trial-and-error process of material discovery is slow and labor-intensive. This hampers the development of urgently needed new technologies for clean energy and environmental sustainability, as well as for electronics and biomedical 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, by creating a novel 3D printing method that produces materials in ways that conventional manufacturing can’t match. The new process mixes multiple aerosolized nanomaterial inks in a single printing nozzle, varying the ink mixing ratio on the fly during the printing process. This method – called high-throughput combinatorial printing (HTCP) – controls both the printed materials’ 3D architectures and local compositions, and can also produce materials with gradient compositions and properties at microscale spatial resolution. Zhang and his team report the new method in a paper 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 Zhang, combining combinational materials printing and high-throughput characterization can significantly accelerate materials discovery. His team 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 can produce functionally graded materials that gradually transition from stiff to soft. Such graded materials should be particularly useful for biomedical applications that need to bridge between soft body tissues and stiff wearable and implantable devices.
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 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.
This story is adapted from material from the University of Notre Dame, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.