Home » Researchers develop ‘gamechanger’ 3D printing method that speeds up new material discoveries

Researchers develop ‘gamechanger’ 3D printing method that speeds up new material discoveries

by Liam Turner
An illustration of high-throughput combinatorial 3D printing

Researchers at the University of Notre Dame in Indiana, USA, have created a novel 3D printing method that they say produces materials in ways that conventional manufacturing “can’t match”.

The 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 process mixes multiple aerosolised nanomaterial inks in a single printing nozzle, which allows for varying of the ink-mixing ratio during the printing process.

Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame, said: “It usually takes 10 to 20 years to discover a new material.

“I thought if we could shorten that time to less than a year — or even a few months — it would be a gamechanger for the discovery and manufacturing of new materials.”

According to the researchers, the aerosol-based HTCP is extremely versatile and applicable to a broad range of metals, semiconductors, 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 characterisation can “significantly” accelerate materials discovery.

His team has already used this approach to identify a semiconductor material with superior thermoelectric properties, a discovery that could be used for energy harvesting and cooling applications.

HTCP also produces functionally graded materials that gradually transition from stiff to soft.

Going forward, Zhang and the students at his Advanced Manufacturing and Energy Lab plan to apply machine learning and AI-guided strategies to the data-rich nature of HTCP in order to accelerate the discovery and development of a broad range of materials.

Zhang said: “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.”

Image: An illustration of high-throughput combinatorial 3D printing. Credit: College of Aerospace and Mechanical Engineering, University of Notre Dame

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