A novel 3D printing method called high-throughput combinatorial printing (HTCP) has been created that significantly accelerates the discovery and production of new materials.
The process involves mixing multiple aerosolized nanomaterial inks during printing, allowing finer control over the architecture and local compositions of the printed materials. This method produces materials with gradient compositions and properties and can be applied to a variety of materials including metals, SemiconductorsPolymers and Biomaterials.
The Edisonian trial-and-error discovery process is slow and laborious. This hinders 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 — that would be a game changer for the discovery and production of new materials.”
Now Zhang has done just that, creating a novel 3D printing method that produces materials in a way that traditional 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 the 3D architectures and local compositions of printed materials, and produces materials with gradient compositions and properties at microscale spatial resolution.
His research was published in the journal May 10, 2023 Nature.
Aerosol-based HTCP is very versatile and applies to metals, semiconductors, and dielectrics, as well as polymers and biomaterials. It creates combination materials that act as “libraries,” each containing thousands of unique compositions.
Combining the high-throughput nature of combinatorial materials printing can significantly accelerate materials discovery, Zhang said. His team has already used this approach to realize a semiconducting material with high thermoelectric properties, a promising discovery for energy harvesting and cooling applications.
In addition to speeding up detection, HTC also makes functionally graded materials that gradually change from hard to soft. This makes them particularly useful in biomedical applications where they need to provide a bridge between soft body tissues and wearable and implantable devices.
In the next phase of research, Zhang and 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 wide range of materials.
“In the future, I hope to develop an autonomous and self-driving process for materials detection and device manufacturing, so students in the lab can focus on higher-level thinking,” Zhang said.
Reference: Minxiang Zeng, Yipu Du, Qiang Jiang, Nicholas Kempf, Chen Wei, Miles V. Bimrose, ANM Tanveer, Hengrui Xu, Jiaho Chen, Dylan J. Kirsch, Jose Kirsch, Jose Kirsch, Jose Kirsch, Brian C. Wyatt, Tatsunori Hayashi, Mortaza Saidi-Javash, Hirotaka Sakou, Babak Anasori, Lihua Jin, and Michael D. McMurtry, Yanliang Zhang, 10 May 2023, Nature.