An innovative 3D printing method has emerged as a “game changer” in the field of materials discovery and manufacturing. Developed by Yanliang Zhang, an associate professor at the University of Notre Dame, the High Performance Combinatorial Printing (HTCP) technique enables the creation of materials that conventional manufacturing methods cannot match.
By combining multiple aerosolized nanomaterial inks into a single print nozzle and adjusting the ink mix ratio during the printing process, HTCP enables precise control over 3D architectures and compositions of printed materials.
The HTCP method has enormous potential to speed up the discovery of new materials, which is usually a slow and laborious process.
“Usually it takes 10 to 20 years to discover a new material,” said Yanliang Zhang, an 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 manufacture of new materials.”
Mixing at the Microscale
The ability to rapidly produce materials with gradient properties and compositions at microscale resolution opens up exciting possibilities for various industries, including clean energy, electronics, and biomedical devices.
The versatility of the HTCP method extends to a wide range of materials, including metals, semiconductors, dielectrics, polymers, and biomaterials. By generating combinational materials that function as libraries with thousands of unique compositions, HTCP offers a powerful tool for material discovery. In fact, Zhang and his team have already taken advantage of this technique to identify a semiconductor material with exceptional thermoelectric properties, a significant advance for energy harvesting and cooling applications.
Furthermore, HTCP is capable of producing functionally classified materials that exhibit a gradual transition from rigid to soft. This feature makes them particularly valuable in biomedical applications that require compatibility between soft tissue and rigid portable or implantable devices.
Accelerating with AI
Looking ahead, Zhang plans to combine HTCP with machine learning and artificial intelligence to further accelerate material discovery and development. By leveraging the data-rich nature of HTCP, his team aims to create a self-contained, self-contained process for material discovery and device fabrication, freeing researchers to focus on higher-level thinking.
You can read the full research paper, titled “High-throughput printing of combinatorial materials from aerosols” in Nature, at this link.
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